Performance of White Sucker Populations along the Saint John River Main Stem, New Brunswick, Canada: An Example of Effects-Based Cumulative Effects Assessment
Bibliographic record
Abstract
Abstract White sucker (Catostomus commersoni) are widely distributed in North America and are often used in environmental monitoring. Whole organism characteristics of three white sucker populations determined to be resident (outside of spawning) within small sections of the Saint John River, New Brunswick, were studied in 2001 and 2002. Significant differences in performance characteristics were present among sites. The differences can be interpreted as either improved sucker performance at Florenceville (upstream site), or decreased performance at Woodstock. Without further investigation it is difficult to identify whether the apparent improved performance is a response to nutrient enrichment, or increased mortality associated with the recent prevalence of lesions. Confounding factors are also present. Daily water level fluctuations resulting from an upstream dam discharge may change habitat availability and/or diversity, thereby altering the fish community. Liver sizes in Saint John River white sucker are considerably larger than in fish collected in Ontario, but are not relative to nearby New Brunswick river populations. This has implications for the importance of reference site selection and understanding the natural variability within a species (intra-specific variation) on multiple spatial scales.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".