Microsatellite data, boundaries of subpopulation centers, and estimated effective migration for greater sage-grouse collected in western North America between 1992 and 2015 (ver. 2.0, December 2022)
Bibliographic record
Abstract
Greater Sage-grouse were sampled for genetic analysis with the goal of quantifying genetic structure and gene flow across the entire species range in the U.S. and Canada. Data presented here consist of two data sets both including genetic data from 15 microsatellite markers. Most samples were collected between 2005 and 2015 (feathers collected non-invasively off the ground) yet some samples were blood samples collected as part of telemetry studies. The samples from Washington were collected much earlier beginning in 1992. The first data set (GRSG_data_dups_full_lek_alias.csv) is the full data consisting of 6,725 individuals. The second data set (GRSG_data_dups_thinned_lek_alias.csv) includes a subset of the full data (2134 individuals) that was thinned in order to equalize sampling effort across the range. Data thinning followed Row et al. 2018.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; a candidate call from one teacher head, not a consensus.
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".