Examination of the responses of slimy sculpin (<i>Cottus cognatus</i>) and white sucker (<i>Catostomus commersoni</i>) collected on the Saint John River (Canada) downstream of pulp mill, paper mill, and sewage discharges
Bibliographic record
Abstract
As part of a larger survey on cumulative effects within the Saint John River basin (Canada), a fish survey was conducted near Edmundston (NB, Canada) in the fall of 1999 using slimy sculpin (Cottus cognatus) and white sucker (Catostomus commersoni). The discharge environment receives effluent from the pulp mill, a paper mill, three sewage discharges, and tributaries receiving agricultural runoff. Sculpin collected downstream of the sewage discharges and pulp mill effluent had greater growth, condition, and liver size but no significant differences in gonad size. Stable isotope data indicated slimy sculpin did not move between sites. Female sculpin collected downstream of the paper mill showed no significant differences in length, body weight, age, condition factor, liver size, and gonad size compared to fish from reference sites. Female white sucker collected downstream of the pulp mill did not differ significantly in any measured parameter compared to reference fish. Liver sizes of white sucker from the Saint John River were outside the range considered to be indicative of uncontaminated riverine sites. In 2000, sculpin collected downstream from a poultry-processing facility had larger livers and lower condition factors, suggesting that the site is contaminated. We found no significant differences in sculpin length, weight, condition (except for males), and liver size in sculpin collected downstream from the pulp mill in October 2001. The responses of slimy sculpin and white sucker differed, perhaps in relation to differences in life history characteristics. Results from this study indicate the slimy sculpin is a suitable fish species for monitoring rivers that receive multiple industrial and municipal effluents.
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How this classification was reachedexpand
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 | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".