An effects-based assessment of slimy sculpin (Cottus cognatus) populations in agricultural regions of northwestern New Brunswick. Water Quality Research
Bibliographic record
Abstract
Recently in Atlantic Canada, there has been increased concern associated with potato farming due to an increase in the fre-quency and magnitude of fish kills downstream of agricultural cultivation activities following major storm events. Over a period of three years (1999–2001), we monitored the population structure and physiological performance of slimy sculpin (Cottus cognatus) within the Little River, located in an intensive potato cultivation region of northwestern New Brunswick. The slimy sculpin, a small-bodied benthic fish, was considered suitable for monitoring due to its natural abundance through-out the system, limited mobility, lack of commercial fishing pressures, and easily measured life history characteristics. Rather than focus on particular agricultural stressors, an effects-based assessment of the fish in the system was conducted to deter-mine whether there were observable and persistent responses of sculpin in the agricultural region. We found that the local population structure at agricultural sites consisted of fewer young-of-the-year sculpin in 2 of 3 fall collections. In comparison with forested reaches, adult sculpin were larger but with smaller gonads, and females had smaller livers, gonads, and fewer and smaller eggs. These biological responses were reduced in the fall of the third year following drier conditions than the previous two years. The effects-based approach successfully demonstrated biological impacts on sculpin both temporally and spatially and therefore the species ’ potential for studying non-point source impacts in environmental monitoring. Key words: effects-based assessment, agriculture, non-point source pollution, slimy sculpin
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".