Bibliographic record
Abstract
LARGE-SCALE, LONG-TERM PROGRAMS to monitor bird abundance have provided the foundation for many of our most successful programs to study and conserve bird populations (Brown et al. 2001, Williams et al. 2002, Kushlan et al. 2002, Rich et al. 2004, U.S. Fish and Wildlife Service 2004). Those programs help identify species at risk and limiting factors, suggest and help evaluate management approaches, and document recovery at the regional and rangewide scale. It is difficult to think of a major wildlife issue for which monitoring has not provided essential information. Yet despite the critical role of bird monitoring programs, many of them are poorly designed and coordinated, and many improvements could be made at relatively low cost. In a welcome addition to the bird-monitoring literature, Conway and Gibbs (2005) describe improved methods for surveying secretive marsh birds. Their study is notable because it is based on >16,000 point counts contributed, at the authors' request, by 15 cooperators working on 12 species in 10 states. Only by recruiting collaborators (they wrote to more than 100 authors) could Conway and Gibbs have compiled such a large and spatially extensive database on that relatively unknown group of birds. Their specific question was whether secretive marsh birds are best monitored by broadcasting calls, listening passively, or doing both. Most of their collaborators used both methods, so Conway and Gibbs (2005) used differences in numbers recorded during passive and active periods, thereby excluding extraneous sources of variation such as site, observer, and weather. They also adjusted results to enable comparison of numbers that would have been recorded with periods of equal duration. They compared number recorded per
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".