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Record W7127228859

Rare, Threatened and Endangered Zoology Species of Oregon (2025)

2025· article· W7127228859 on OpenAlexaboutno aff
Jesse A. Laney, Eleanor P. Gaines, Lindsey Wise, Misty Nelson

Bibliographic record

VenuePDXScholar (Portland State University) · 2025
Typearticle
Language
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsEndangered speciesThreatened speciesNatural heritagePopulationBiodiversityConservation statusNatural (archaeology)Natural resource
DOInot available

Abstract

fetched live from OpenAlex

This publication provides a 2025 update to the rare, threatened, and endangered zoology species of Oregon. Species lists are also provided in Excel format. The Oregon Biodiversity Information Center (ORBIC) is part of the Institute for Natural Resources (INR) located at Portland State University (PSU). ORBIC maintains extensive databases of Oregon biodiversity, concentrating on rare and endangered plants, animals and ecosystems. Since its creation in 1979 as the Oregon Natural Heritage Program, ORBIC has been part of the Natural Heritage network. ORBIC is a constituent member of NatureServe, a non-profit organization with a mission to provide the scientific basis for effective conservation action. NatureServe and Oregon manage data using standards and protocols used across the U.S., Canada, and much of Latin America. ORBIC has a primary mission to track the distribution and status of all of Oregon’s flora and fauna, including all vertebrates and vascular plants, and as many invertebrates, fungi, and lichens as is possible. For species considered to be at-risk in Oregon or across the planet, location and population data is managed for all of the observations and occurrences in the state. These data, and the knowledge of the many professional and amateur biologists across the state, provide the basis of the status information in this list.

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 score0.110
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1100.037

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 head0.017
GPT teacher head0.208
Teacher spread0.191 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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