The biology and ecology of slimy sculpin: A recipe for effective environmental monitoring
Bibliographic record
Abstract
Recently, the use of small-bodied fish in environmental monitoring has increased, particularly within the Canadian environmental effects monitoring (EEM) and other adaptive programs. Although it is possible to measure changes with many small-bodied species, interpretation is often complicated by the absence of information on the biology and ecology of fish not of commercial, recreational, or traditional interest. Knowing and understanding the basic biology of these fishes aids in the sensitivity of study designs (i.e., ability to detect change) and the interpretation of all biological levels of responses (e.g., cellular to community). The increased use of slimy sculpin ( Cottus cognatus Richardson, 1836) in impact assessment studies in North America provides a considerable amount of information on life history aspects. The slimy sculpin has the most ubiquitous North American distribution among cottids but yet has a very small home range, thus integrating environmental conditions of localized areas. This paper describes aspects of slimy sculpin life cycle that affect collection efficiency and timing, and describes and provides data collected over more than 10 years of studies at more than 20 reference study sites. This overview provides a functional and informative compilation to support adaptive environmental monitoring and provide a baseline for comparative ecological study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".