Obligation to Enhance OBIS Data for Sea- and Shorebirds of the Americas
Bibliographic record
Abstract
The distributions of many sea- and shorebird species span large geographic areas, making them ideal candidates as biomonitors of ecosystem perturbations and long-term environmental trends. The basic question examined in this study was: Does a major open-access data archive contain sufficient temporal- and spatial-scale data to support more detailed inquiry into multi-decadal-scale responses in geographic distributions of specific taxa? The global-scale open-access data platform, Ocean Biodiversity Information System (OBIS), was searched to compile data on bird distributions of the Americas, including the Caribbean Sea. More than 680,000 occurrence records of 210 species, collected between 1965 and 2018, were located and evaluated by marine ecoregion. The Puget Trough/Georgia Basin marine ecoregion, along the United States/Canadian border, and the Virginian marine ecoregion on the US east coast, dominated occurrences, each with more than 100,000 records, while the Gulf of Maine/Bay of Fundy had the most years of records (42). Most records from South America (~29,000) came from the Channels and Fjords of Southern Chile, collected across 16 different years. More than 90% of the recorded data were collected since 1983, and more than 95% of the records were from North American marine ecoregions. We urge additional observations to be shared via OBIS to allow comprehensive large-scale and detailed meta-analyses of spatial and temporal trends in marine and shore-bird communities and their biodiversity.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".