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Record W2592217868 · doi:10.1139/facets-2015-0010

A life-table model estimation of the parr capacity of a late 19th century Puget Sound steelhead population

2016· article· en· W2592217868 on OpenAlexvenueno aff
Nick Gayeski, George R. Pess, Tim Beechie

Bibliographic record

VenueFACETS · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersNorthwest Fisheries Science CenterNational Marine Fisheries ServiceNational Oceanic and Atmospheric Administration
KeywordsTributarySound (geography)HabitatDrainage basinRange (aeronautics)ShoreHydrology (agriculture)Rainbow troutEnvironmental scienceFisheryPopulationTable (database)Critical habitatCurrent (fluid)GeographyFish <Actinopterygii>EcologyGeologyOceanographyBiologyCartographyEngineeringEndangered species

Abstract

fetched live from OpenAlex

An age-structured life-cycle model of steelhead ( Oncorhynchus mykiss) for the Stillaguamish River in Puget Sound, Washington, USA, was employed to estimate the number of age-1 steelhead parr that could have produced the estimated adult return of 69 000 in 1895. We then divided the estimated parr numbers by the estimated area of steelhead rearing habitat in the Stillaguamish River basin in 1895 and under current conditions to estimate density of rearing steelhead then and now. Scaled to estimates of total wetted area of tributary and mainstem shallow shoreline habitat, our historic estimates averaged 0.39–0.49 parr·m −2 , and ranged from 0.24 to 0.7 parr·m −2 . These values are significantly greater than current densities in the Stillaguamish (mainstem average: 0.15 parr·m −2 , tributaries: 0.07 parr·m −2 ), but well within the range of recent estimates of steelhead parr rearing densities in high-quality habitats. Our results indicate that modest improvement in the capacity of mainstem and tributary rearing habitat in Puget Sound rivers will yield large recovery benefits if realized in a large proportion of the area of river basins currently accessible to steelhead.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.214
Teacher spread0.198 · 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 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
Published2016
Admission routes1
Has abstractyes

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