Sediment grain size sampling and analysis within the Canadian Aquaculture Monitoring Program
Bibliographic record
Abstract
The grain size distribution of the inorganic fraction of bottom sediment can be used to characterize aquaculture sites, assess changes in sedimentation resulting from aquaculture, and to study the transport and deposition of deleterious substances. This report provides science-based advice to the Aquaculture Monitoring Program (AMP) of Fisheries and Oceans Canada on best practices related to sampling methods, laboratory analysis, and data interpretation for sediment grain size (SGS). This report complements Milligan et al. (2024) that concluded that the Beckman Coulter LS 13 320 laser diffraction instrument (LS) should not replace the Coulter Counter (CC) within the AMP to determine if changes to SGS are occurring. The LS is unable to determine the amount of material deposited in flocs limiting it to site assessment and monitoring the accumulation of fine-grained inorganic sediment in individual areas. It does not have the ability to compare different regions. The LS would not be effective for developing a nationally consistent database nor to study and model the transport of deleterious substances. Due to the natural variation of SGS, annual sampling of surficial sediments is not recommended. Instead, cores with undisturbed sediment water interfaces should be collected to determine changes in sedimentation over time.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".