The challenges of standardizing colonial waterbird survey protocols - what is working? What is not?
Bibliographic record
Abstract
Our ability to manage and conserve colonial waterbird species throughout Mexico, Meso-America, Canada, the Caribbean nations, and the United States is presently hampered by a lack of reliable information on the status and trends of their populations, information that can only be obtained by collecting comparable data using standardized data collection techniques that estimate bias. The U.S. Geological Survey, Patuxent Wildlife Research Center is working in concert with the North American Waterbird Monitoring Partnership (Kushlan et al. 2002) to coordinate waterbird monitoring efforts and to develop an agreed-upon set of survey methods that incorporate bias estimation. To determine the practicality of implementing methods that require measures of detection probability and to test the error associated with specific survey methods prior to their adoption as standards, several tests have been conducted the field.
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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 | 0.706 | 0.719 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.013 | 0.008 |
| Research integrity | 0.007 | 0.011 |
| 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; the direct Gemma label and the distilled Codex classifier 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".