Review of <i> Freshwater Ecoregions of North America: A Conservation Assessment</i> by Robin A. Abell, David M. Olson, Eric Dinerstein, Patrick T. Hurley, et al.
Bibliographic record
Abstract
Defining ecogregions as "relatively large areas of land or water that contain a geographically distinct assemblage of natural communities," this book documents the efforts of the World Wildlife Fund-United States to identify areas with aquatic habitats in the United States, Canada, and Mexico that support "globally outstanding biological diversity." Public and private conservation groups can then focus their efforts on preserving the aquatic ecosystems of the most globally significant areas. The book opens with its authors' discussion of their use of a biological distinctiveness index, focusing on fish, mussels, and crayfish species, to delineate the ecoregions of North America. Much of the information used to evaluate and rank ecoregions is based on expert opinion. Environmental threats and the conservation status of each ecoregion are then evaluated. Finally, ecoregions that are highest priorities for conservation because of their global significance are identified. Ecoregion-based conservation approaches are advocated in the last chapter, the authors arguing that evaluation within an ecoregion should focus on distinct habitats, large examples of intact habitat, keystone habitats, and large-scale ecological phenomena (such as animal migration). This ecoregion-level assessment, however, should be initiated first in those ecoregions that have been identified as globally outstanding.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".