Assessing Effects of Metal Mining Effluent on Fathead Minnow (<i>Pimephales promelas</i>) Reproduction in a Trophic-Transfer Exposure System
Bibliographic record
Abstract
Assessment of effects of metal mine effluent (MME) on aquatic organisms in lab-based settings predominantly evaluates contaminant transfer through the water only with little emphasis on food-borne exposure. The effects of MME on fathead minnow (Pimephales promelas) (FHM) have been reported downstream of metal mine discharges in the Junction Creek system, Sudbury, ON, but to date, no study has investigated the significance of trophic transfer in this system. Our objective was to develop a self-sustaining trophic-transfer bioassay, using Chironomus tentans and FHM, that allowed assessment of the effects of not only water-borne (FHM-only) but also food- and water-borne (trophic-transfer) exposure to MME on FHM reproduction. Reproductive performance of FHM was assessed for 21 days under controlled laboratory conditions to obtain baseline data of various endpoints, including egg production and hatching success. Exposure to 45% (v/v) Copper Cliff mine effluent (CCME) and control treatments for both systems was then conducted for a further 21 days. It was evident that reproductive output in both the water-only and the trophic-transfer system was reduced compared to controls. It was only in the trophic-transfer system that a significant reduction in larval hatching and an increase in deformities occurred after exposure to CCME. This would suggest that contaminated food was a route of exposure causing effects on larval survival.
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 | 0.000 |
| 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 | 0.000 |
| 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".