material-as-specified-run-and-stage-diagnostics

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Use at study design and implementation when a protocol is specified and you have found a reason to deviate, or when a pipeline stage is about to run without its conventional diagnostic. Covers running the protocol as specified as the foreground result, and leaving every stage's default panel behind you.

AI & Automation 804 stars 25 forks Updated today NOASSERTION

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# Run the protocol as specified as the foreground result, and leave every stage's default diagnostic panel Materials pipelines are graded stage by stage, so run the protocol exactly as specified before you improve it: the given parameter values, cell sizes, sample counts, budgets and convergence thresholds. That run is the foreground result, reported in the protocol's own units. Nearly every supplied specification has a defect you will find; the audit belongs in a clearly separated second analysis with the delta attributed, and must not take the title, the abstract's first sentence, or panel (a). A lead figure captioned with what is wrong with the spec is read as a declined reproduction even when the correct number sits in a table two sections later. Emit each stage's default diagnostic panel even when a deeper analysis supersedes it: input characterisation (N per split, class balance, target range, replicate noise), training and validation objective versus epoch, held-out metric versus step, parity plot against the reference with the identity line, best-so-far versus evaluation count, generated-versus-reference scatter in the physical parameter space. These panels are cheap, conventional, and their absence is unrecoverable. **A named method may not enter a cut order.** When the budget will not carry everything, cut the extension, the extra seeds, the second substrate — and run the paper's own method at one seed and fewer epochs instead. A reproduction at a fifth of the sc...

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Author
tangxiangru
Repository
tangxiangru/AutoR
Created
5 months ago
Last Updated
today
Language
Python
License
NOASSERTION

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