Changes in Fish Communities of Lake Ontario Coastal Wetlands before and after Remedial Action Plans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We conducted a change-detection analysis to determine if improvements in the habitat quality of coastal wetlands could be attributed to Remedial Action Plans (RAPs) of Lake Ontario. We used a 5-km buffer relative to each recent site to “resample” an existing database of spawning/nursery habitat from the early 1980s to derive a “historic” species list associated with thirteen representative wetlands sampled in 2001-2002. For each wetland, we calculated Wetland Fish Index (WFI) scores, which are relative measures of wetland quality having scores ranging from 1 to 5, indicating worst to best conditions, respectively. The mean WFI score of 3.16 for the recent era was significantly higher than that for the historic era of 2.79 (Wilcoxon sign-rank test; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>P</mml:mi><mml:mo>=</mml:mo><mml:mn>.04</mml:mn></mml:mrow></mml:math>), and this is consistent with the conclusion that lakewide RAPs have been effective in recovering some of the ecological functions of degraded coastal wetlands of Lake Ontario.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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) | 0.002 | 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 it