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Record W2140273735 · doi:10.1007/s10641-009-9462-y

Movements of brown smoothhounds, Mustelus henlei, in Tomales Bay, California

2009· article· en· W2140273735 on OpenAlexaff
Brent R. Campos, Maxfield A. Fish, Gardner J. Jones, R. W. Ford and G. D. Riley, Peter J. Allen, Peter Klimley, Joseph J. Cech, John T. Kelly

Bibliographic record

VenueEnvironmental Biology of Fishes · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsUniversity of Manitoba
FundersUniversity of California, DavisDivision of Agriculture and Natural Resources, University of California
KeywordsDiel vertical migrationBayMovement (music)Environmental scienceOceanographyGeologyPhysics

Abstract

fetched live from OpenAlex

Ultrasonic telemetry was used to analyze the effects of environmental variables on movement directions and movement rates of brown smoothhounds, Mustelus henlei , in Tomales Bay, California. Ultrasonic transmitters were surgically implanted in the peritoneal cavities of one male and five female brown smoothhounds and tracked during the period of 29 June to 15 July 2004. Coarse-scale tracking consisted of locating all tagged individuals multiple times during a single session, while fine-scale tracking consisted of following a single individual continuously during a session. Coarse-scale tracking suggested movement toward the inner bay with incoming and high tides and toward the outer bay with outgoing and low tides (P = 0.01), whereas the diel cycle had no apparent effect on their movement directions. Mean shark movement rate was 0.09 m s −1 (range: 0.01–0.34 m s −1 ), with diel and tidal cycles both having significant effects on their rates of movement (P = 0.02 and P < 0.01), respectively. We tracked two female sharks on a fine scale over three tracking sessions in July 2004. Both individuals exhibited higher rates of movement during the night compared to the day ( P < 0.01). While one shark’s rate of movement was not significantly affected by tidal stage, the other’s was ( P < 0.001).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.207
Teacher spread0.200 · 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.

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

Citations40
Published2009
Admission routes1
Has abstractyes

Explore more

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