Variables affecting sediment sulfide concentrations in regulatory monitoring at salmon farms in the Bay of Fundy, Canada
Bibliographic record
Abstract
Annual monitoring of sediments is conducted under salmon farms in the southwestern New Brunswick (SWNB) area of the Bay of Fundy, Canada, from August to October.We examined the relationships between the average sediment sulfide concentrations at salmon farms monitored from 2006 to 2009 and some variables related to farm operations: farm age, predicted average near-surface current speed, and estimated biomass of salmon at the time of monitoring.Data for all of these variables were available for 87% of salmon farms monitored in these years (farms that had been inactive for >1 yr were excluded).The year of monitoring had no significant effect, so data from all 4 yr were combined.The ability of the 3 variables to predict sulfide concentrations at the time of monitoring was analyzed using a linear model with log-transformation of variables (except farm age).Each variable individually showed a significant correlation with sulfide concentration, but the model including all 3 variables explained only 37% of the variation.Current speed and biomass explained the highest proportions of sulfide variation (together 35%).Almost 30% of monitoring events occurred at farms holding no fish.When these fallowed sites were excluded, the model explained only 24% of sulfide variation, with current speed being the most important predictor variable.Management actions targeted at farm size (biomass) and physical aspects of sites (especially current speed) may help to reduce the risk of causing adverse benthic impacts, but measurable effects may not be observed due to the large amount of sulfide variation that is not explained by these variables.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".