Revisiting the influence of top-down and bottom-up pressures on Wa hia hé:ta (yellow perch <i>Perca flavescens</i> Mitchill, 1814) population dynamics in Kaniatarowanenneh (the Upper St. Lawrence River): implications for collaborative research
Bibliographic record
Abstract
Kaniatarowanenneh (the St. Lawrence River) is the outflow of one of the world’s largest freshwater ecosystems and its ecological health has implications for resource management. The population dynamics of an ecologically and economically important fish, the Wa hia hé:ta, Mohawk for yellow perch ( Perca flavescens), are considered by including data that extend to the past century to redress temporal gaps in comparative literature. We found both a significant top-down effect from piscivorous fish as well as a significant bottom-up effect related to total phosphorus on yellow perch relative abundance in the Lake Ontario–Upper St. Lawrence system. Regarding the bottom-up effect, the current state of yellow perch reflects the population size prior to cultural eutrophication (pre-1940s/50s) likely responding to the re-oligotrophication of the system. These findings emphasize the importance of considering historical records in fish population dynamics research to incorporate shifting population baselines into fisheries management. The study also demonstrates the need for collaborative approaches that bring critical new insights and multivocality.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".