Assessment of net change of productive capacity of fish habitats: the role of uncertainty and complexity in decision making
Bibliographic record
Abstract
Canada's fish habitat management is guided by the principle of "no net loss of the productive capacity of fish habitat" (NNL). Many development proposals are assessed using habitat information alone, rather than fish data. Because fishhabitat linkages are often obscured by uncertainty, uncertainty must be factored into NNL assessments. Using a quantitative framework for assessing NNL and lake habitats as a context, the implications of uncertainty for decision making are examined. The overall behaviour of a net change equation given uncertainty is explored using Monte Carlo simulation. Case studies from Great Lakes development projects are examined using interval analysis. The results indicate that uncertainty, even when large, can be incorporated into assessments. This has important implications for the habitat management based on NNL. First, schemas to specify relative levels of uncertainty using simple habitat classifications can support robust decision making. Second, attaining NNL requires greater emphasis on minimizing habitat loss and creating new areas to compensate for losses elsewhere and less on detailing small incremental changes in modified habitats where the fish response is difficult to demonstrate. Third, the moderate to high levels of uncertainty in fishhabitat linkages require that created compensation is at least twice the losses to reasonably ensure NNL.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".