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Record W2208782263

1998 warmwater fish survey of Vancouver Lake, Clark County

2021· book· en· W2208782263 on OpenAlexaboutno aff
Stephen J. Caromile, William R. Meyer, Chad S. Jackson

Bibliographic record

VenueAquaDocs (United Nations Educational, Scientific and Cultural Organization) · 2021
Typebook
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFisheryChannel (broadcasting)ShoreFish <Actinopterygii>FishingHabitatWater qualityPopulationGeographyOpen waterPopulation dynamics of fisheriesHydrology (agriculture)Environmental scienceEcologyGeology
DOInot available

Abstract

fetched live from OpenAlex

The Vancouver Lake warmwater fish population was sampled during the fall of 1998. Vancouver Lake is a large lake, connected directly to the Columbia River through the Lake River to the north and the constructed flushing channel on the eastern shore. A total of 16 species of fish were encountered, mostly warmwater game fish with a few seasonal visitors. The lake has suffered from water quality problems, and during the 1970s, it was dredged, and a channel was created to aid in increasing the water turnover rate, effectively connecting both ends of the lake to the Columbia River. As there is direct access to the Columbia River, managing this lake as a closed system may prove difficult. It is of our opinion that it may be a more effective use of resources to increase the anglers access to the available resources through providing improved boat launch access, and fish habitat structure throughout the lake. Additionally, educating the public about the how-to's of common carp angling could possibly open up additional resources not commonly utilized by the public. (Document pdf contains 38 pages)

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)0.000
metaresearch head score (Gemma)0.000
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 score0.910
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.002

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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