Impacts of nonpoint inputs from potato farming on populations of slimy sculpin (<i>Cottus cognatus</i>)
Bibliographic record
Abstract
The potential influence of agricultural activity, particularly potato cultivation, on slimy sculpin populations (Cottus cognatus) was examined at 19 rivers of New Brunswick, Canada. Comparisons with forested streams resulted in differences in fish density, size, and reproductive performance. Young-of-the-year (YOY) sculpin were present only at two of 11 agricultural sites, though they were present at all nine forested sites. Sediment deposition was greatest at agricultural sites, with increased fine sediments deposited. Larger, coarse sands were deposited at two sites with active forest operations. Temperature had a stronger correlation than sedimentation with sculpin size and density in the agricultural region. Agricultural catchments were warmer than in forested catchments (median = 16.0 and 13.3 degrees C, respectively). Body size of slimy sculpin was correlated positively and YOY densities correlated negatively with temperature, and sites with temperatures > or = 25 degrees C were devoid of YOY sculpin. Our data indicate there is a significant effect of temperature on slimy sculpin populations in rivers of potato farming areas, highlighting the importance of examining indirect factors when investigating possible impacts of nonpoint source agricultural inputs. Indirect factors such as sediment deposition and temperature need to be considered in order to discriminate accurately the chronic impacts of agricultural chemicals on fish populations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".