Role of flocculation in the spatial and temporal variation in organic matter flux at an active salmon aquaculture site in a deep fjord
Bibliographic record
Abstract
Sediment traps with programmable bottles collected settling material in a deepwater fjord on the southwest coast of Newfoundland at active salmon aquaculture sites and at sites 1000 m away from operations. Both the total and organic fluxes were up to an order of magnitude larger at the 2 aquaculture sites compared to the 2 sites 1000 m away. Stable isotopes, organic matter %, % carbon, and grain size were used to characterize the transport of aquaculture-derived waste material away from active sites. Floc fraction, a process-based parameterization of the disaggregated inorganic grain size of collected sediment, was used to show that flocculation was the dominant process in controlling the deposition of suspended particulate matter. Up to 79% of the material deposited was flocculated. Stable isotope and organic carbon analysis of the deposited material indicated that aquaculture waste products could be elucidated 1000 m from operations, consistent with other studies. In this fjord setting, the simplistic model AutoDEPOMOD was unable to predict the amount of organic matter deposition that was observed in our sediment traps. Better model parameterization is required in order to confidently simulate and manage the effects of finfish aquaculture discharges.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".