MacroRefugia Indices for North American Avifauna
Bibliographic record
Abstract
Macrorefugia metrics for 400 North American breeding bird species and two time periods were developed based on the approach described in Stralberg et al. (2018), available at DOI: 10.1111/geb.12731 Climate-change refugia indices were generated for individual species based on species distribution model predictions from Bateman et al. (2020) available at https://www.audubon.org/climate/survivalbydegrees, documented Biotic velocity(Carroll et al., 2015) for each species is calculated using the nearest-analog velocity algorithm defined by (Hamann et al., 2015)and then applies the distance-decay function to obtain an index ranging from 0 to 1. For a fat-tailed distribution (c = 0.5, and alpha = 8333.33) results in a mean migration rate of 500 m/year (50km/century). Refugia index values are averaged over three GCMs (CCSM4, GFDLCM3, INMCM4). The values of the final product have been multiplied by 100 to create smaller integer files.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads 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".