Nesting cormorants and temporal changes in Island habitat
Bibliographic record
Abstract
ABSTRACT Double‐crested cormorant (Phalacrocorax auritus) populations have increased greatly across North America. The interior North America subpopulation is the largest with many birds nesting on the Laurentian Great Lakes. Lake Erie supports a large number of breeding pairs that nests primarily on islands in the western basin of the lake. These islands also harbor many rare plant species constituting some of the last vestiges of Carolinian plant communities in Canada. Nesting cormorants can adversely affect the plant communities on the islands on which they nest. Annual ground censuses were conducted from 1979 to 2011 to assess temporal changes in the density of nesting cormorants on 3 islands in western Lake Erie. We used aerial photographs taken over a maximum 16‐year span to assess changes in forest cover on these island ecosystems. We observed declines in forest cover on all 3 islands ranging from 47% to 85%. Trends among islands differed reflecting differences in cormorant colonization histories and the degree to which cormorants were managed, thereby influencing nest densities. Islands without cormorant management had cormorant nest densities ranging from approximately 300–500 nests/ha and forests declined continuously through time. On the island where cormorants were culled, nest densities were lower (approx. 200 nests/ha) and forest decline stabilized. © 2014 The Wildlife Society.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".