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Record W6929667665 · doi:10.5063/f1v40snm

Designcraft for experiments: Solo Surveys

2021· dataset· en· W6929667665 on OpenAlexaboutno aff

Bibliographic record

VenueUC Santa Barbara · 2021
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsReplicateSet (abstract data type)CitationFocus (optics)Data collectionWork (physics)

Abstract

fetched live from OpenAlex

A set of remote exercises exploring experimental design for the life sciences. A total of three independent labs were done to focus on different design principles, sampling techniques, and taxa. Exercises are described in full in this open access manual entitled Designcraft for experiments. Data were collected by students in a third-year biology course offering at York University in Toronto, ON, Canada. Students collected the data and published individual datasets to figshare with a CCBY4.0 licensing. Data were then compiled for each lab including balcony birdwatching, backyard bioblitz, and solo surveys. The provenance of each independent observation in the compiled data was recognized by attribution to the initials of the primary data author including citation via DOI and link to figshare.AttributionCJL designed the experiments.Students in course offering collected, innovated, and published individual, primary datasets.SH and CJL reviewed the compiled data.MetadataRecord-levelyear is the calendar year of work not termexperiment describes the three experimental options in designcraft booksource is either full experiment or pilot experimentrep is the replicate per experimental setlocation is the site name locallyhabitat is the ecological classificationdate is the calendar date for each observation

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.297
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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