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Record W2141121131 · doi:10.1641/b580507

Freshwater Ecoregions of the World: A New Map of Biogeographic Units for Freshwater Biodiversity Conservation

2008· article· en· W2141121131 on OpenAlexaff
Robin Abell, Michele Thieme, Carmen Revenga, Mark Bryer, Maurice Kottelat, N.G. Bogutskaya, Brian W. Coad, Nick E. Mandrak, Salvador Contreras Balderas, William A. Bussing, Melanie L. J. Stiassny, Paul Skelton, Gerald R. Allen, Peter J. Unmack, A.M. Naseka, Rebecca Ng, Nikolai Sindorf, James Robertson, Eric Armijo, Jonathan Higgins, Thomas J. Heibel, Eric Wikramanayake, David M. Olson, Hugo Luis López, Roberto Esser dos Reis, John G. Lundberg, Mark H. Sabaj Pérez, Paulo Petry

Bibliographic record

VenueBioScience · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaCanadian Museum of Nature
Fundersnot available
KeywordsEcoregionFreshwater fishBiodiversityGeographyEcologyHabitatBiogeographyBiologyFish <Actinopterygii>Fishery

Abstract

fetched live from OpenAlex

We present a new map depicting the first global biogeographic regionalization of Earth's freshwater systems. This map of freshwater ecoregions is based on the distributions and compositions of freshwater fish species and incorporates major ecological and evolutionary patterns. Covering virtually all freshwater habitats on Earth, this ecoregion map, together with associated species data, is a useful tool for underpinning global and regional conservation planning efforts (particularly to identify outstanding and imperiled freshwater systems); for serving as a logical framework for large-scale conservation strategies; and for providing a global-scale knowledge base for increasing freshwater biogeographic literacy. Preliminary data for fish species compiled by ecoregion reveal some previously unrecognized areas of high biodiversity, highlighting the benefit of looking at the world's freshwaters through a new framework.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.002

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.

Opus teacher head0.031
GPT teacher head0.208
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2,029
Published2008
Admission routes1
Has abstractyes

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