RNA/DNA ratio and total length of yellow perch (<i>Perca flavescens)</i> in managed and natural wetlands of a large fluvial lake
Bibliographic record
Abstract
RNA/DNA ratios and total lengths were compared to determine growth patterns of age-0 yellow perch (Perca flavescens) in managed and natural habitats of a large fluvial lake (Lake Saint-Pierre, St. Lawrence River, Quebec, Canada) over seasonal and yearly temporal scales. In 2002, the RNA/DNA ratio responded to degree-days accumulated over periods of 78 days before sampling, while in 2003, no relationship with temperature was established. The growth patterns obtained each year probably reflect indices responding to different limiting variables. In 2002, temperature would have been limiting, whereas in 2003, other factors such as prey availability, food quality, and competition may have influenced growth. In addition, the discrepancy between total length and RNA/DNA ratio observed in 2003 may reflect a differential time of response to limiting variables. These results together show that the two indices reflect growth at different time scales and suggest that their combination can help identify shifts between limiting environmental variables. Also, growth in managed wetlands during springtime was systematically superior to that in the natural environment, supporting the contention that managed wetlands are highly productive habitats. In natural habitats, growth rates were higher on the south shore by summer, which is consistent with the established north-south productivity gradient in Lake Saint-Pierre.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".