Indicators for ecological carrying capacity of bivalve and seaweed aquaculture
Bibliographic record
Abstract
Abstract Within the framework of Ecosystem Approach to Aquaculture (EAA), ecological carrying capacity (ECC) is a key concept that helps to determine the upper limit of production without compromising ecosystem functioning. The implementation of ECC is complex as ECC differs between type of farms and location and standardised methods should be developed for management. There is therefore a clear need for operational indicators. The objectives of this paper were: (1) to carry out a systematic literature review on shellfish and seaweed aquaculture‐environment interactions to list the most used environmental indicators, (2) to classify the indicators according to the effects they measure (i.e., benthic, water quality, food web interactions, cultured organism health, resource use) and the scale at which they are applied, and (3) to assess their potential based on four indicator criteria categories: sensitivity, accuracy and precision, feasibility and utility, and ecosystem‐level scalability. Overall, indicators describing benthic effects were the most highly cited and scored. Indicators identified for bivalve and seaweed culture were discussed and compared to previous work on salmon aquaculture indicators to highlight similarities and differences across trophic levels. In addition, questions related to the challenges of ECC indicators implementation were presented to a panel of experts. The scoring and consultation provided the source of discussion on environmental management consistent with EAA.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".