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Record W6987395007

Studies of eulachon in Oregon and Washington: Designed to guide implementation of a monitoring program to track coast-wide status and trends in abundance and distribution. Project Completion Report July 2015 – June 2018.

2018· report· en· W6987395007 on OpenAlexaboutno aff

Bibliographic record

VenueAquaDocs (United Nations Educational, Scientific and Cultural Organization) · 2018
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeTransectPopulationIchthyoplanktonAbundance (ecology)Aerial surveySampling (signal processing)Population densityFish <Actinopterygii>
DOInot available

Abstract

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In 2015, the Oregon Department of Fish and Wildlife and the Washington Department of Fish and Wildlife (WDFW) received a grant to expand on the research and monitoring of Eulachon in Washington and Oregon. We focused on the establishment of a system to track coast-wide status and trends in abundance and distribution of the ESA listed southern Eulachon distinct population segment (DPS). The primary objective was for WDFW to develop annual eulachon spawning stock biomass (SSB) estimates for the Columbia River population based on egg and larval production surveys. We developed survey protocols that estimated egg and larvae density (n/m3) at a transect comprised of six sampling stations crossing the Columbia River just upstream of the estuary. The transect was situated to capture of eggs and larvae produced from all Columbia River spawning areas (mainstem and tributaries) except for the Grays River. Separate sampling stations were located on the Grays River. We combined mean weekly egg and larvae densities with estimated river discharge (m3/s) to estimate the total number of eulachon eggs and larvae produced for specific time periods over five years of eulachon returns to the Columbia River. We converted the estimates of total egg and larvae production into SSB using estimated relative fecundity, sex ratio, and fish weight. We used bootstrapping on the Columbia River data to develop confidence limits for those estimates. Because the trend in the Columbia River SSB estimates should be interpreted relative to the rest of the Southern DPS, we also developed SSB estimates for two watersheds outside the Columbia Basin (the Naselle River and the Chehalis River). The SSB estimates for the Columbia River, Grays River, Naselle River, and Chehalis River were compared to Canadian DFO estimates for the Fraser River. The monitoring of the Chehalis River Eulachon also included the development of a relative abundance model using eDNA in conjunction with larvae density estimates, and the use of eDNA to assess upriver extent of spawning. This eDNA work in the Chehalis River complimented our temporal genetic analysis of the Columbia River run, in that both studies showed a potential for the larval outflow estimates to be biased slightly high due to Longfin Smelt larvae being present during the early part of the outmigration. Since these two species larvae share very similar morphology, the Longfin Smelt larvae are likely assumed to be Eulachon larvae by those counting larvae. In addition to the larval monitoring, we continued to collect samples of adult Eulachon to expand our knowledge about their length, weight, age, sex ratios, and fecundity (information needed to parameterize our SSB estimation model). Combining our SSB estimates with known Eulachon harvest, run estimates were developed for the Columbia River population. Those run estimates were allocated to broodyear tables based on year specific age composition and sex ratio data. We also worked on public outreach and education concerning Eulachon and their importance to the Pacific Northwest.

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)0.003
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.067
GPT teacher head0.421
Teacher spread0.354 · 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
Published2018
Admission routes1
Has abstractyes

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