Coastal zone occupancy by double-crested cormorants on a Laurentian Great Lake before, during, and after a food web regime shift
Bibliographic record
Abstract
The Lake Huron food web has been undergoing change since the invasion of driessenid mussels (Dreissena spp.) (late 1990s), especially in 2003 featuring the lake-wide loss of alewife (Alosa pseudoharengus) among other elements that year. Collectively the changes in 2003 satisfy a number of criteria for a regime shift. Based on multiflight surveys (2001–2005), we modeled coastal zone occupancy of foraging double-crested cormorants (Phalacrocorax auritus) in the North Channel and Georgian Bay of Lake Huron during the regime shift period. At the start of the regime shift (2003), there were a number of plausible occupancy models based on a set of spatial covariates, the only year in the study when this occurred. Annual shifts in the magnitude and sign of coefficients indicated movement of cormorants between the two coastal regions, especially during and immediately after the regime shift period. Declines in cormorant occupancy of coastal habitat were not confined to 2003 but extended through 2004, with declines in occupancy in the North Channel (2003) preceding that in Georgian Bay (2004). Declines in occupancy in offshore areas in both coastal regions preceded declines in nearshore areas, possibly reflecting the loss of alewife at the time. The spatial response of predators or prey in regime shifts could serve as early indicators of tipping points in ecosystems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".