Avian Point-Count Data from Boreal Alaska and Maps of Predicted Population Density for Lesser Yellowlegs, Olive-sided Flycatcher, and Rusty Blackbird, 2001-2020
Bibliographic record
Abstract
This data package contains 1) field data and 2) predicted distributions of three species of boreal-nesting birds in interior Alaska: Lesser Yellowlegs (Tringa flavipes), Olive-sided Flycatcher (Contopus cooperi), and Rusty Blackbird (Euphagus carolinus). The data are compiled from several monitoring programs: Alaska Landbird Monitoring Survey, Alaska Off-road Point-count Program, Susitna-Watana Hydroelectric Project, Tetlin Forest Inventory Analysis, and surveys on Department of Defense lands by the U.S. Fish and Wildlife Service. Each program conducted avian point-count surveys in some or all years (2001-2020) at locations in Alaska. This dataset includes only the locations within Bird Conservation Region 4 (BCR4; Bird Studies Canada and NABCI 2014) and south-central Alaska. The dataset includes a number of remotely-sensed covariates that were compiled from other sources for the survey locations (Alaska Department of Transportation 2018, Alaska Wildland Fire Coordinating Group 2020, Alaska Center for Conservation Science 2017, Porter et al. 2018, PRISM Climate Group 2018a,b). The data package also includes shapefiles showing the predicted population density of each species across BCR4 in Alaska, where predictions were developed by relating observations to covariates to estimate density and then predicting density based on values of covariates across the landscape; and shapefiles delineating hotspots for each species, where a hotspot is defined as a grid cell whose mean predicted density exceeds the means of 90% of other grid cells in BCR4.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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 source (direct Gemma or distilled Codex), 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".