Ecological carrying capacity in mariculture: Consideration and application in geographic strategies and policy
Bibliographic record
Abstract
Governance and management strategies for aquaculture development were examined for a select number of jurisdictions covering a range of marine aquaculture production to better understand the degree to which concepts of “Ecological Carrying Capacity” (ECC) are incorporated into management tools or permitting requirements for aquaculture development. Policies, regulations, and strategic plans were sought through professional knowledge and, at times, using web-based searches. Aquaculture ECC, defined here as, “the magnitude of aquaculture production that can be supported without leading to unacceptable changes in ecological process, species, populations, or communities in the environment,” was not strictly applied in any jurisdiction’s aquaculture policy documentation. A broadened search to consider the concept of aquaculture carrying capacity (CC) more generally was conducted. Of the ten nations examined, CC concepts could be found in policy documentation of several nations. The inclusion of CC concepts in policy and strategic planning can be used as part of a suite of management tools to promote sustainable aquaculture within FAO’s Ecological Approach to Aquaculture.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".