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Record W3178458337 · doi:10.51492/cfwj.cesasi.8

Protecting a displaced species in an altered river: a case study of the endangered Sacramento River winter-run Chinook Salmon

2021· article· en· W3178458337 on OpenAlexfundno aff
Erica M. Meyers

Bibliographic record

VenueCalifornia Fish and Wildlife Journal · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersBureau of ReclamationU.S. Fish and Wildlife ServiceDepartment of Water ResourcesKillam TrustsCalifornia State Water Resources Control BoardCalifornia Department of Fish and Wildlife
KeywordsChinook windEndangered speciesDam removalFish migrationOncorhynchusHabitatWildlifeFisheryPopulationEnvironmental scienceClimate changeCritical habitatHabitat destructionGeographyEcologyFish <Actinopterygii>Biology

Abstract

fetched live from OpenAlex

Endangered Sacramento River winter-run Chinook Salmon (Oncorhynchus tshawytscha) exist as a single population that spawns in the Sacramento River downstream of Shasta Dam near Redding, CA. Displaced from their historical habitat after dam construction circa 1940, their survival depends on cold water released from Shasta Reservoir. Managing and recovering the species is further complicated by their anadromous life history, habitat loss and degradation, largescale water supply management, and climate change. The California Department of Fish and Wildlife and other resource agencies coordinate closely to protect the species from extinction, confronting challenges with collaborative restoration and science-driven management. As climate change brings more frequent droughts, warmer weather, and increased variability in precipitation, Sacramento River winter-run Chinook Salmon recovery will require greater collaboration and a shift to more holistic restoration actions that promote and maintain the diversity and resilience of the species and its habitats.

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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0090.003
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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 designCase report
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

Citations5
Published2021
Admission routes1
Has abstractyes

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