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%%  1. Keep  \anonymoustrue  below.  KJAI uses double-blind peer review, so the
%%     manuscript you upload must not name you anywhere -- including in URLs to
%%     your own repository.  Use an anonymised link during review.
%%  2. Send the separate title page as its own file. It is the only document
%%     that identifies you.
%%  3. Complete the DECLARATIONS block and the REPRODUCIBILITY CHECKLIST in
%%     Appendix A.  A research article without both is returned at screening.
%%  4. Send BOTH the .tex source and the compiled .pdf to
%%     admission@thekoreanacad.com  with the subject line
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@article{vaswani2017,
  author  = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki},
  title   = {Attention is all you need},
  journal = {Advances in Neural Information Processing Systems},
  year    = {2017},
  volume  = {30},
  pages   = {5998--6008}
}
@book{murphy2022,
  author    = {Murphy, Kevin P.},
  title     = {Probabilistic Machine Learning: An Introduction},
  year      = {2022},
  publisher = {MIT Press},
  address   = {Cambridge, MA}
}
@incollection{mitchell2019,
  author    = {Mitchell, Margaret and Wu, Simone and Zaldivar, Andrew},
  title     = {Model cards for model reporting},
  booktitle = {Proceedings of the Conference on Fairness, Accountability and Transparency},
  pages     = {220--229},
  year      = {2019},
  publisher = {ACM},
  address   = {New York, NY}
}
@misc{dataset2025,
  author       = {{Anonymous Consortium}},
  title        = {A worked example dataset for applied {AI} reporting},
  year         = {2025},
  howpublished = {Zenodo},
  note         = {Persistent identifier: 10.xxxx/zenodo.xxxxxxx}
}
\end{filecontents}

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                         Evidence from a Multi-Site Clinical Deployment}
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%%----------------------------------------------------------------------------
%%  ABSTRACT  --  150-250 words, no citations, no undefined abbreviations
%%----------------------------------------------------------------------------
\begin{kjaiabstract}{Abstract}
State the problem, the data, the method, the main quantitative finding, and why it
matters --- in that order. Report the headline result with its uncertainty, not as a
bare point estimate. Do not cite anything here, and do not use an abbreviation that
has not been spelled out. Aim for 150 to 250 words. A good abstract can be read on its
own and still make sense; a reader who never opens the article should come away knowing
what you found, on what data, and how confident you are.

If your paper applies AI to a domain, say which domain in the first sentence. A reader
scanning this journal is deciding whether your work speaks to their field, and the
abstract is where they decide.
\end{kjaiabstract}

\keywords{applied machine learning; distribution shift; calibration; clinical decision
support; model evaluation}

\vspace{0.6em}
\noindent{\color{kjaisky}\rule{\textwidth}{0.4pt}}
\vspace{0.8em}

%%=============================================================================
%%  BODY OF THE ARTICLE
%%=============================================================================

\section{Introduction}

Open with the question this article answers. Say what is already known, name the gap
your work fills, and close the section with your specific aim, research questions or
hypotheses. Cite as you go \cite{vaswani2017,murphy2022}.

Because KJAI is a cross-domain journal, write the introduction for two readers at once:
someone who knows the method but not your domain, and someone who knows the domain but
not the method. Define the domain problem in plain terms before you formalise it.

Word limits, excluding references and appendices, are: Research Article 6{,}000--8{,}000;
Applied Study or Case Report 4{,}000--6{,}000; Systems, Dataset or Resource Paper
3{,}000--5{,}000; Short Communication 2{,}000--3{,}500; Review or Survey 6{,}000--10{,}000;
Book, Tool or Dataset Review 1{,}000--1{,}500. Name your section on the title page and in
\verb|\papersection| above.

\subsection{Contributions}

State your contributions explicitly, as a short list. Claim only what the paper
demonstrates.

\begin{enumerate}[leftmargin=*,itemsep=2pt,topsep=4pt]
  \item A method, stated precisely enough to be reimplemented.
  \item An evaluation on named data, with the comparison that makes the result meaningful.
  \item An analysis of when the method fails, which is usually the most useful part.
\end{enumerate}

\section{Related Work}

Review only the work your argument actually rests on. A related-work section is an
argument about the state of a field, not an inventory of it. Group by idea rather than
by year, and end each group by saying what it leaves unresolved.

\section{Method}

\subsection{Problem formulation}

Give the formal setting before the algorithm. Define your notation once, in one place,
and use it consistently. Let $\mathcal{D} = \{(x_i, y_i)\}_{i=1}^{n}$ be a dataset of
$n$ examples drawn from a distribution $P(X, Y)$, and let $f_\theta : \mathcal{X} \to
\mathcal{Y}$ denote a model with parameters $\theta$.

\begin{definition}[Calibration error]
For a model producing probability estimates $\hat{p}$, the expected calibration error is
the average absolute gap between confidence and accuracy across $M$ equal-width bins.
\end{definition}

\subsection{Algorithm}

Present the procedure as pseudocode. It should be complete enough that a competent
reader could implement it without guessing, and short enough that they will read it.

\begin{algorithm}[htbp]
\caption{Recalibration under covariate shift}
\label{alg:recal}
\begin{algorithmic}[1]
\Require Source data $\mathcal{D}_s$, unlabelled target data $\mathcal{D}_t$, model $f_\theta$
\Ensure Recalibrated model $f_{\theta'}$
  \State $w \gets \textsc{EstimateImportanceWeights}(\mathcal{D}_s, \mathcal{D}_t)$
  \State $\mathcal{D}_v \gets \textsc{WeightedSplit}(\mathcal{D}_s, w, \text{frac}=0.2)$
  \For{$T$ in candidate temperatures}
    \State $\ell_T \gets \textsc{WeightedNLL}(f_{\theta}, \mathcal{D}_v, T, w)$
  \EndFor
  \State $T^{*} \gets \arg\min_T \ell_T$
  \State \Return $f_{\theta'}$ with logits scaled by $1/T^{*}$
\end{algorithmic}
\end{algorithm}

Refer to the algorithm by number in the text, as in Algorithm~\ref{alg:recal}. State its
computational complexity and any assumption it relies on --- here, that the support of
the target distribution is contained in that of the source.

\subsection{Implementation}

Where a short code fragment communicates something prose cannot, include it. Do not
paste an entire codebase into the article; put that in the repository and cite it.

\begin{lstlisting}[language=Python,caption={Weighted temperature scaling, abridged.},label={lst:temp}]
def weighted_temperature(logits, labels, weights, grid):
    """Return the temperature minimising weighted NLL on a held-out split."""
    best_t, best_loss = 1.0, float("inf")
    for t in grid:
        loss = weighted_nll(logits / t, labels, weights)
        if loss < best_loss:
            best_t, best_loss = t, loss
    return best_t
\end{lstlisting}

\section{Experimental Setup}

This section exists so that someone else can repeat your work. Be specific.

\subsection{Data}

Name every dataset, its version or release date, its size, its licence, and how it was
obtained \cite{dataset2025}. State the split strategy and whether the split is random,
temporal or by site --- for applied work the difference usually matters more than the
model does. Where data concerns people, state the consent basis and the approving ethics
body here in a form that does not identify you; the full statement with names and
reference numbers goes on the separate title page.

\subsection{Baselines and comparisons}

A result without a baseline is not a result. Say which baselines you compare against,
why those, and whether you tuned them with the same budget you gave your own method. If
you did not, say so.

\subsection{Training and hyperparameters}

Report the optimiser, learning-rate schedule, batch size, number of epochs, early-stopping
criterion, hardware, framework versions, random seeds and the number of repeated runs.
A table is usually the clearest way to do this. Report the compute used, in GPU-hours or
equivalent, and where the search space was larger than the results table suggests, report
the full search.

\subsection{Evaluation metrics}

Define every metric, including the ones you think are obvious --- ``accuracy'' is
ambiguous the moment classes are imbalanced. Say what uncertainty you report (standard
deviation over seeds, bootstrap confidence interval, or an analytic interval) and how
many runs it summarises.

\section{Results}

\subsection{Main results}

Tables use \texttt{booktabs}: three horizontal rules, never a vertical one. The caption
goes \emph{above} the table. Refer to every table in the text before it appears ---
see Table~\ref{tab:main}.

\begin{table}[htbp]
  \centering
  \caption{Performance across three deployment sites. Mean over five seeds, with standard
           deviation. ECE is expected calibration error (lower is better).}
  \label{tab:main}
  \small
  \begin{tabularx}{\textwidth}{@{}lXrrr@{}}
    \toprule
    Method & Setting & AUROC & ECE & Brier \\
    \midrule
    Baseline           & Source site only            & .871\,(.006) & .094\,(.011) & .118\,(.004) \\
    Baseline           & Transferred, no adaptation  & .804\,(.014) & .173\,(.022) & .162\,(.009) \\
    Temperature scaling& Transferred                 & .804\,(.014) & .081\,(.013) & .139\,(.006) \\
    \textbf{This work} & Transferred, weighted       & \textbf{.812\,(.011)} & \textbf{.038\,(.007)} & \textbf{.127\,(.005)} \\
    \midrule
    \multicolumn{2}{@{}l}{\textit{Oracle (target labels used)}} & \textit{.829} & \textit{.021} & \textit{.119} \\
    \bottomrule
  \end{tabularx}
  \par\vspace{0.4em}
  {\footnotesize\textit{Note.} $N = 24{,}118$ encounters across three sites. Bold marks the best
  non-oracle result in each column. Discrimination is largely unchanged; the gain is in calibration.}
\end{table}

Say in the text what the table shows, and be honest about what it does not. Here,
discrimination did not improve --- writing ``our method improves performance'' would be
a misreading of the authors' own result.

A table that runs over a page break should use \texttt{longtable}. Keep tables readable
in black and white: do not encode anything in colour alone.

\subsection{Figures}

The caption goes \emph{below} the figure. Supply figures as vector PDF where possible, or
at a minimum 300\,dpi for photographs and screenshots. Label axes, give units, and make
the figure legible at the size it will actually be printed. Refer to each one in the text
before it appears, as in Figure~\ref{fig:example}.

\begin{figure}[htbp]
  \centering
  % Replace the framed box below with your own figure:
  % \includegraphics[width=0.8\textwidth]{myfigure.pdf}
  \fbox{\begin{minipage}[c][4.5cm][c]{0.8\textwidth}\centering
    \color{kjaigrey}\itshape Replace this box with \texttt{\textbackslash includegraphics}
  \end{minipage}}
  \caption{A short declarative caption that says what the reader should see, not merely
           what the figure is called.}
  \label{fig:example}
\end{figure}

\subsection{Ablations and sensitivity}

Show which parts of the method are doing the work. An ablation that changes nothing is
worth reporting --- it tells the reader what to leave out.

\subsection{Equations}

Set short expressions in line, such as $p < .05$ or $\mathcal{L}(\theta)$. Display and
number anything you refer to later:

\begin{equation}
  \label{eq:ece}
  \mathrm{ECE} = \sum_{m=1}^{M} \frac{|B_m|}{n}\,\bigl|\,\mathrm{acc}(B_m) - \mathrm{conf}(B_m)\,\bigr|
\end{equation}

\noindent where $B_m$ is the set of examples falling in the $m$-th confidence bin, $n$ the
total number of examples, and $\mathrm{acc}(\cdot)$ and $\mathrm{conf}(\cdot)$ the accuracy
and mean confidence within a bin. Always define every symbol immediately after the equation.
Refer back to it by number, as in Equation~\eqref{eq:ece}.

Multi-line derivations use \texttt{align}, with alignment on the relation:

\begin{align}
  \mathcal{L}(\theta) &= -\frac{1}{n}\sum_{i=1}^{n} w_i \log p_\theta(y_i \mid x_i) \label{eq:loss}\\
                      &\quad + \lambda \lVert \theta \rVert_2^2 \nonumber
\end{align}

Use \verb|\nonumber| on lines you will not cite.

\section{Discussion}

Say what the findings mean, how they answer the question in Section 1, and --- just as
importantly --- what they do not show. Distinguish what your evidence supports from what
you believe. If your result is smaller than you hoped, say so; a paper that reports a
modest effect honestly is more useful than one that oversells a large one.

\subsection{Implications for practice}

For an applied paper, this is often the section a domain reader turns to first. What
should someone in the field do differently, if anything, and under what conditions?

\section{Limitations}

Every study has them. Naming yours strengthens the article; hiding them invites a
reviewer to name them for you. Cover at least: the scope of the data, the generality of
the setting, the assumptions the method depends on, and the failure modes you observed.

\section{Broader Impact and Ethical Considerations}

Applied AI is deployed on people. State the foreseeable consequences of this work being
used, including the ones you would rather not think about: who is affected by a false
positive, who by a false negative, and whether those two populations are the same. If a
misuse is plausible, name it, and say what in the design makes it harder. If you believe
the risks are negligible, say that and say why --- do not leave the section out.

\section{Conclusion}

Short. What is now known that was not known before, and what should be done next.

%%=============================================================================
%%  DECLARATIONS  --  all six are required. Keep the headings.
%%=============================================================================
\section*{Declarations}
\addcontentsline{toc}{section}{Declarations}
\small

\noindent\textbf{Code and data availability.}
State where the code and the data can be found, with a persistent identifier, or explain
precisely why they cannot be shared. ``The clinical dataset cannot be released because
consent was given for use within the treating institution only; the analysis code is at
[anonymised link during review]'' is a perfectly acceptable answer. Silence is not.

\smallskip
\noindent\textbf{Generative AI use.}
Name the tool, the version, and what it was used for --- or write ``No generative AI tools
were used in the preparation of this manuscript.'' Language polishing counts and must be
declared. Where a generative model is part of the method rather than the writing process,
describe it in the Method section as well. AI cannot be listed as an author, and the
authors remain responsible for every statement in the paper.

\smallskip
\noindent\textbf{Ethics approval and consent.}
Give the approving body and reference number, or state why approval was not required.
Confirm that no participant is identifiable in this article.

\smallskip
\noindent\textbf{Funding.}
Name every funder and grant number, or state that the work received no specific funding.

\smallskip
\noindent\textbf{Conflicts of interest.}
In the submission version write: ``Declared on the title page.'' This file must stay
anonymous.

\smallskip
\noindent\textbf{Compute and environmental footprint.}
Report the total compute used for the work as finally reported, including failed runs
where you can estimate them, in GPU-hours or equivalent, with the hardware type. Where
you can, state the region or provider, which determines the carbon intensity.

\normalsize

%%=============================================================================
%%  REFERENCES
%%=============================================================================
\printbibliography[title={References}]

%%=============================================================================
%%  APPENDICES
%%=============================================================================
\appendix

\section{Reproducibility Checklist}
\small
Answer every line. ``No'' is an acceptable answer with a reason; a blank is not.

\begin{tabular}{@{}p{0.72\textwidth}l@{}}
  \toprule
  Item & Answer \\
  \midrule
  All claims in the abstract and introduction are supported by the results & Yes / No \\
  The setting, assumptions and limitations of the method are stated        & Yes / No \\
  Every dataset is named, with version, size, licence and provenance       & Yes / No \\
  The train/validation/test split strategy is stated                       & Yes / No \\
  All hyperparameters and the search space are reported                    & Yes / No \\
  The number of runs and the definition of reported uncertainty are stated & Yes / No \\
  Hardware, framework versions and total compute are reported              & Yes / No \\
  Code is available, or its unavailability is explained                    & Yes / No \\
  Data is available, or its unavailability is explained                    & Yes / No \\
  Baselines received a comparable tuning budget                            & Yes / No \\
  Ethics approval and consent are stated where human data is used          & Yes / No / N/A \\
  Use of generative AI is declared                                         & Yes / No \\
  Foreseeable misuse and differential harms are discussed                  & Yes / No \\
  \bottomrule
\end{tabular}
\normalsize

\section{Additional Results}
Extended tables, further ablations, hyperparameter grids and full search results go here.

\section{Notation}
\small
\begin{tabular}{@{}ll@{\hspace{2.5em}}ll@{}}
  $\mathcal{D}$   & dataset                & $f_\theta$ & model with parameters $\theta$ \\
  $x_i, y_i$      & input and label        & $w_i$      & importance weight of example $i$ \\
  $P(X,Y)$        & data distribution      & $T$        & calibration temperature \\
  $B_m$           & $m$-th confidence bin  & $\lambda$  & regularisation coefficient \\
\end{tabular}
\normalsize

\end{document}