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Record W4394195693 · doi:10.6084/m9.figshare.20557296

Survival data for analysis

2022· dataset· en· W4394195693 on OpenAlexaffabout
Joanie L. Kennah, Michael J. L. Peers, Eric Vander Wal, Yasmine N. Majchrzak, Allyson K. Menzies, Emily K. Studd, Rudy Boonstra, Murray M. Humphries, Thomas S. Jung, Alice J. Kenney, Charles J. Krebs, Stan Boutin

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldMathematics
TopicStatistical Methods and Inference
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

These datasets contain survival data for snowshoe hares collected in the springs of 2015-2018, between March 1 and May 31 and in the falls of 2015-2018, between September 1 and December 1. Survival data were obtained by conducting telemetry checks on VHF-collared hares. Various covariates of interest are included, such as weather metrics and coat colour mismatch values. We used these datasets to assess the influence of coat colour mismatch on snowshoe hare autumn and spring survival. These data were collected in the Kluane Lake region of the Yukon. <br>

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)0.000
metaresearch head score (Gemma)0.043
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
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 score0.970
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.9700.000

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 head0.508
GPT teacher head0.493
Teacher spread0.015 · 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
Published2022
Admission routes2
Has abstractyes

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