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Record W7042308820

PHOTOPHASE AND ILLUMINATION EFFECTS ON THE SWIMMING PERFORMANCE AND BEHAVIOR

2004· article· en· W7042308820 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Supply Chain Traceability
Canadian institutionsnot available
FundersKillam TrustsMassachusetts Department of Fish and GameCalifornia Department of Fish and Game
KeywordsFlumeChinook windSmeltGaitFish <Actinopterygii>
DOInot available

Abstract

fetched live from OpenAlex

Photophase and illumination affect many fish activities. In this study, we examined their effects on the critical swimming velocities (Ucrit), swimming gait patterns, and oral grasping behavior of five California estuarine fishes. All species (4-5 cm SL) swam similarly (mean Ucrit range:30-36 cm/s) under day/light conditions. However, both nighttime photophase and darkness decreased Delta Smelt (Hypomesus transpacificus) swimming velocities. Congeneric Wakasagi (H. nipponensis) swimming performance also decreased at night/dark conditions. Regardless of photophase and illumination, Delta Smelt, Wakasagi, and Splittail (Pogonichthys macrolepidotus) exhibited three swimming gaits: intermittent stroke-and-glide at low velocities, continuous stroking at moderate velocities, and intermittent burst-and-glide at high velocities near Ucrit. In contrast, Chinook Salmon (Onchorhynchus tshawytscha) used only two swimming gaits: continuous stroking and burst-and-glide under all conditions. Inland Silversides (Menidia beryllina) used these two gaits under light conditions and all three gaits under dark conditions. Some Wakasagi, Splittail, and Chinook Salmon orally grasped the upstream screen in the flume at moderate to high water velocities. Oral grasping does not require jaw teeth and may represent adaptive behavior in natural habitats. Regarding vulnerability to water diversions that operate in the dark at night, the threatened Delta Smelt and introduced Wakasagi, comparatively, may be more at risk than the other species.

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 score0.435
Threshold uncertainty score0.552

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 communication0.0010.001
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 head0.010
GPT teacher head0.188
Teacher spread0.177 · 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
Published2004
Admission routes1
Has abstractyes

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