Compensation ratios needed to offset timing effects of losses and gains and achieve no net loss of productive capacity of fish habitat
Bibliographic record
Abstract
Minns' (Can. J. Fish. Aquat. Sci. 54: 24632473 (1997)) framework for assessing net change of productive capacity of fish habitats in Canada is expanded to include the effect of timing of losses and gains on cumulative net change. The expansion requires establishment of a reference time frame for assessment. A time frame of twice the project's duration is recommended. Delaying compensation actions while incurring losses early in a project increases the levels of compensation required. The addition of future discounting had much less effect on compensation requirements than the effects resulting from timing differences between losses and compensation. As discounts apply equally to losses and gains, they likely balance out over time. Delays between when habitat alterations occur and when expected productive capacity is attained increase the required compensation. There are advantages to starting compensation efforts early in a development project. A case study of a hypothetical northern diamond mine shows how various components of compensation (replacement, uncertainty, and timing) can be integrated when assessing net change. Consideration of all components of compensation indicates the need for tougher precautionary compensation guidelines with ratios greater than the current 1:1. Values of 2:1 or higher may be necessary to ensure attainment of Canada's guiding policy principle of no net loss.
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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 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".