Monitoring methods to support area-based bivalve aquaculture management in the Pacific region
Bibliographic record
Abstract
The Pacific Shellfish Aquaculture Management Division (AMD) of Fisheries and Oceans Canada (DFO) requested recommendations regarding monitoring methodologies along with associated field and laboratory protocols that can be used by regulatory, industry and science personnel when carrying out environmental assessments. The sampling methods put forward in this report are intended to support a wide variety of approaches ranging from general area based monitoring programs or local emerging issues associated with a significant knowledge gap. A suite of environmental variables that support bivalve aquaculture assessments was selected based on the following: 1) recommendations arising from government advisory processes and/or the scientific community; and 2) the ability of the indicator to detect potential shifts in ecosystem conditions and processes. The benthic variables selected include sediment texture, geochemical (e.g. organic, redox), macrofaunal, meiofaunal, and epifaunal attributes, while pelagic variables consist of both physical (temperature, salinity, dissolved oxygen, light) and biotic characteristics (phytoplankton, zooplankton). Relevant bivalve attributes include cultured and wild density, diversity, and condition indices. The pelagic and bivalve indicators represent a nutrient-seston-plankton-bivalve loop that can support a high-resolution, spatially explicit, hydrodynamic-biogeochemical coupled model capable of evaluating ecological bivalve carrying capacity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".