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Record W2009956303 · doi:10.1111/jai.12088

Assessment of spatio-temporal variation in larval abundance of lake sturgeon (<i>Acipenser fulvescens</i>) in the Rupert River (Quebec, Canada), using drift nets

2012· article· en· W2009956303 on OpenAlexaffabout
R. Verdon, J C Guay, M. La Haye, Marc Simoneau, A. Côté-Bherer, Nelson Ouellet, Marc Gendron

Bibliographic record

VenueJournal of Applied Ichthyology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsGDG EnvironnementHydro-Québec
Fundersnot available
KeywordsAcipenserLake sturgeonTransectTributaryBayAcoustic Doppler current profilerLarvaHydrology (agriculture)IchthyoplanktonFisheryBiologyOceanographyAbundance (ecology)SturgeonCurrent (fluid)EcologyGeologyFish <Actinopterygii>Geography

Abstract

fetched live from OpenAlex

The Rupert River is one of the largest tributaries on the east coast of James Bay. Lake sturgeon (Acipenser fulvescens) is present all along the main stem where several spawning grounds have been located, four of which are major spawning grounds that have been studied at km 216, 281, 290 and 362. The total number of drifting larvae was estimated with drift nets set along transverse transects at km 212, 276, and km 287 from 2007 to 2009, and at km 361 in 2008 and 2009, using a new technique, namely, a Doppler current meter to measure water velocity within transect sub-sections corresponding to Voronoï polygons. There was a substantial, persistent difference in the number of larvae produced by the four main spawning areas. On average, the most productive site (km 276) produced over five times more larvae than the least productive site (km 361). Average estimated numbers were 41,194 at km 212, 176,840 at km 276, 106,212 at km 287, and 30,642 at km 361. Temporal variations were of much less amplitude than spatial differences. Between 2007 and 2009, interannual variations were not significant, except at km 212, despite differences in river flow during incubation and larval drift. The number of gravid females and the quality of spawning grounds would likely be the main factors influencing the total number of larvae. Vertical distribution of larvae is variable between sites and years, and shows a slight tendency for larvae to be more surface oriented. Higher flow near the surface would partly explain larger surface drifting of larvae. Transverse distribution is uneven and often associated with the location of the spawning grounds and the river flow. Given the uneven vertical and transverse distribution of larvae, an effective sampling strategy should cover the complete water column and full river width. Where depth exceeds 3 m, at least two stacked nets are recommended. In large rivers, filtering close to 1% of total river flow should result in acceptable confidence intervals, allowing a good comparison of the number of larvae in space and time.

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 score0.104
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.233
Teacher spread0.223 · 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

Citations13
Published2012
Admission routes2
Has abstractyes

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