A methodology for identifying and classifying aquatic biodiversity investment areas: Application in the Great Lakes basin
Bibliographic record
Abstract
Abstract A scientifically defensible methodology for identifying areas of high biodiversity in aquatic environments is presented. Areas of high biodiversity or Aquatic Biodiversity Investment Areas are identified using a technique referred to as Habitat Supply Analysis. This technique uses the microhabitat features of an ecosystem in conjunction with information on the microhabitat preferences of fish to calculate the suitability of an area to fish. The method is structured so that the suitability of habitat to lifestages of fish, species of fish, groups of fish and fish assemblages can be evaluated. The methodology recognises that to some degree all areas within an aquatic system contribute to the maintenance of biodiversity. As such, a classification scheme is proposed to evaluate the potential versus the actual contribution of an area to the maintenance of biodiversity in an ecosystem. This classification scheme is designed to help prioritise habitat restoration and preservation efforts. Prototype evaluations of the methodology for identifying and classifying Aquatic Biodiversity Investment Areas are presented.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".