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Record W4394234584 · doi:10.6084/m9.figshare.21431011

Additional file 2 of Realized niche shift of an invasive widow spider: drivers and impacts of human activities

2022· dataset· en· W4394234584 on OpenAlex
Zhenhua Luo, Monica A. Mowery, Xinlan Cheng, Qing Yang, Junhua Hu, Maydianne C. B. Andrade

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpider Taxonomy and Behavior Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpiderNicheJaguarGeographyBiologyEcology

Abstract

fetched live from OpenAlex

Additional file 2. Table S2: Performances of the AUS climatic models of Latrodectus hasselti with the randomkfold (k = 10) partition method. Table S3: Performances of the AUS climatic models of Latrodectus hasselti with the block partition method. Table S4: Performances of the AUS climatic models of Latrodectus hasselti with the checkerboard1 partition method. Table S5: Performances of the AUS climatic models of Latrodectus hasselti with the checkerboard2 partition method. Table S6: Performances of the AUS full models of Latrodectus hasselti with the randomkfold (k = 10) partition method. Table S7: Performances of the AUS full models of Latrodectus hasselti with the block partition method. Table S8: Performances of the AUS full models of Latrodectus hasselti with the checkerboard1 partition method. Table S9: Performances of the AUS full models of Latrodectus hasselti with the checkerboard2 partition method. Table S10: Performances of the INV climatic models of Latrodectus hasselti with the randomkfold (k = 10) partition method. Table S11: Performances of the INV climatic models of Latrodectus hasselti with the block partition method. Table S12: Performances of the INV climatic models of Latrodectus hasselti with the checkerboard1 partition method. Table S13: Performances of the INV climatic models of Latrodectus hasselti with the checkerboard2 partition method. Table S14: Performances of the INV full models of Latrodectus hasselti with the randomkfold (k = 10) partition method. Table S15: Performances of the INV full models of Latrodectus hasselti with the block partition method. Table S16: Performances of the INV full models of Latrodectus hasselti with the checkerboard1 partition method. Table S17: Performances of the INV full models of Latrodectus hasselti with the checkerboard2 partition method.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.958
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.9580.000

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 head0.027
GPT teacher head0.274
Teacher spread0.247 · 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