Development of a Monitoring Design for Examining Effects in Wild Fish Associated with Discharges from Metal Mines
Bibliographic record
Abstract
Abstract As part of the amended Metal Mining Liquid Effluent Regulations under the Fisheries Act, mines will be required to develop and conduct Environmental Effects Monitoring (EEM). EEM will be done to evaluate the effects of mine effluent on fish, fish habitat, and fish usability. Mines will be required to determine if there are changes in fish populations and/or in the usability of fish due to mine effluent. The EEM program has been designed with a tiered monitoring approach, with the first phase determining if an effect is present. Subsequent phases of EEM will focus on continued monitoring and determining the magnitude, geographic extent, and cause of effects (if any). Fish collected from the area exposed to mine effluent will be compared to fish from a reference area in order to determine if there is an effect, if the effect is mine related and the cause of the effect within the effluent. The fish population survey will examine the growth, reproduction, condition, and survival of one or more resident sentinel fish species. Fish usability will be determined based on the appearance of fish, their use, and the contaminant levels in fish tissue. It is recognized that some mines may not be able to implement a fish monitoring program as outlined, so it has been recommended that alternative methods, such as a caged bivalves or on-site bioassays, may also be used. Frequency of monitoring will be dependent on the previous results of the fish and benthic invertebrate monitoring phases.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".