Additional file 1: of Collective interaction effects associated with mammalian behavioral traits reveal genetic factors connecting fear and hemostasis
Bibliographic record
Abstract
Table S1. Sample sizes of behavioral trait data considered for outbred mice8 and dogs5. Figure S1 Distributions of quantitative trait values used for association testing. See Table S1. c, e, h, and i have been log-transformed (c and e from percentages, and h and i from a scale of 1 to 5). Figure S2 Comparison of single-SNP p-values from linear regression (LR) and continuous discriminant analysis (CDA). Figure S3 Comparison of empirical p-values of groups of interacting SNPs estimated by permutation of phenotype labels (symbols) and the use of the null distribution of R for normally distributed data. Figure S4 Optimized prediction scores of 10 top-ranked pathways for fear conditioning (FC; cued test). Figure S5 Population stratification of Labrador Retrievers using principal component (PC) analysis. Figure S6 Quantile-quantile plots of independent SNP p-values for Labrador Retriever data. Figure S7 Independent-SNP and collective association levels of SNPs in pathways highly ranked for fear in dogs. Figure S8 Top-ranked pathways for fear of humans/objects (dogs). Table S2 Additional pathways from Reactome database (Feb. 2018) ranked by association strengths with respect to fear conditioning (cued test). (PDF 1811 kb)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.860 | 0.153 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".