[W] SeeTheWork

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Illustrative scores · independent of your workspace edits

ML research · Example candidate

Reproduce a research claim within a compute budget

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Task outcome

Made the experiment traceable; one recorded run failed.

Delivered Reproduction + research verdict

Evaluation score3.8/5Illustrative score

Score breakdown

Expand to see why
Reproducibility4.0/5

Records seeds, settings and run outcomes. Environment versions are still needed.

Experimental design4.0/5

Plans paired comparisons and accounts for compute. The study is limited to one dataset.

Scientific judgment5.0/5

Reports the failed attempt and limits the recommendation to the available evidence.

Investigation follow-through2.0/5

Leaves the failed run undiagnosed and the next experiment unprioritized.

How to read these scores

1 = major gaps · 3 = partially meets · 5 = fully meets. This example weights all four criteria equally.

These scores and excerpts are illustrative, not a live evaluation or a hiring recommendation. In the product, evaluations score submitted work against the configured criteria; missing evidence is identified separately.

Full findings and sources
01

Builds a reproducible experiment

Supported in this example

02

Makes a controlled comparison

Supported in this example

03

Calibrates an adoption recommendation

Follow-up needed

Optional follow-up

Which next experiment would most change your adoption recommendation within the remaining budget?

Source [01] · Illustrative excerpt

Experiment manifest

Record the conditions.

experiment.yaml

seeds: [11, 23, 37]
training_steps: 1200
data_split: fixed_split_v1
methods: [baseline, proposed]
record: [configuration, runtime, metric, failures]
What the source supports
The manifest states the settings needed to interpret and repeat the comparison.
What remains open
An executable environment and dependency versions are also needed for reproduction.