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Record W7005462147

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.

2002· article· en· W7005462147 on OpenAlexaboutno aff

Bibliographic record

VenueLincoln (University of Nebraska) · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Diversity and Evolution
Canadian institutionsnot available
Fundersnot available
KeywordsEcoregionCrayfishWildlifeHabitatBiomeNatural (archaeology)Wildlife conservation
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.192
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2002
Admission routes1
Has abstractyes

Explore more

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