Distinguishing among remediation, reclamation, and offsetting in the pursuit of no net loss
Bibliographic record
Abstract
The mitigation hierarchy (avoid - minimize - remediate - offset) is a well-accepted framework for prioritizing impact mitigation measures. The roles of three kinds of habitat remediation are sometimes confused. Here we set out to distinguish among and compare "third step remediation" (3SR), the third step in the mitigation hierarchy, end-of-project reclamation/rehabilitation, and offsetting. The essential criteria for 3SR to contribute to no net loss of biodiversity are quality, quantity and timeliness, the last emphasizing the need to address biodiversity losses in a compressed timeframe. Using these criteria, we distinguish 3SR from end-of-project rehabilitation/reclamation and from biodiversity offsetting, the fourth step of the mitigation hierarchy, and offsetting from end-of-project work. We briefly review two policies from Canada and Australia which unhelpfully blur these distinctions. We seek to provide greater clarity to assist all forms of mitigation measures to contribute optimally to the conservation of biodiversity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".