Data and code for: Delineating ecologically distinct groups for annual cycle management of a declining shorebird
Bibliographic record
Abstract
Cleaned data and code used to conduct analyses for Knight et al. 2025. Delineating ecologically distinct groups for annual cycle management of a declining shorebird. Journal of Applied Ecology. Data provided is daily locations for 148 individual long-billed curlews (Numenius americanus) that have been interpolated from high-resolution Argos or GPS satellite tags. Data have been segmented into seasons and migration stopovers following the methods described in the manuscript. Scripts are available in the compressed "code" folder and should be run in numbered order using the "LBCU_FilteredData_Segmented_Manuscript.csv" input data file and the compressed AtlasRegionsShp folder provided in this repository. Running this code will will download additional geospatial data from eBird, Google Earth Engine, and the National Oceanic and Atmospheric Administration, some of which will require registering for accounts to access the data. The compressed "GroupPolygons" folder is the final management regions for long-billed curlews recommended by the paper.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.105 | 0.081 |
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".