2002. Quantifying impact of pulp mill effluent on fish in Canadian marine and estuarine environments: problems and progress. Water Quality Research
Bibliographic record
Abstract
Amendments to the federal Pulp and Paper Effluent Regulations in effect since 1992, require mills in Canada discharging effluent to an aquatic receiving envi-ronment to conduct an Environmental Effects Monitoring (EEM) Program to determine whether existing regulations adequately protect fish, fish habitat and use of fisheries resources. As one component of the EEM, mills measure indices of growth, survival and reproduction in wild-caught fish exposed to effluent and compare them with fish not exposed to effluent. A review of the first round of Fish Surveys (Cycle 1: 1993–1996) indicated that they contributed useful data in the freshwater receiving environments for which they had been designed, but performed poorly in the more complex marine and estuarine environments. The most prevalent and serious problems were that insufficient fish were caught and the degree of exposure to effluent could not be quantified. Recommendations to address these problems in Cycle 2 (1997–2000) included selection of small-bodied, presumably more sedentary, fish and studies on the alternative approaches: caged bivalves and onshore bioassays (mesocosms).
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".