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Record W229290935

The Distribution and Abundance of Northern Shrimp (Pandalus borealis) in Relation to Bottom Temperatures in NAFO Divisions 3LNO Based on Multi-Species Surveys from 1995-2002.

2002· article· en· W229290935 on OpenAlexaboutno aff
Northwest Atlantic, Eugene Colbourne, David Orr

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsShrimpSpring (device)Abundance (ecology)FisheryDecapodaRange (aeronautics)HabitatGeographySpatial distributionOceanographyEnvironmental scienceEcologyCrustaceanBiologyGeologyPhysics
DOInot available

Abstract

fetched live from OpenAlex

The spatial distributions and abundance of northern shrimp are presented in relation to their thermal habitat for NAFO Divisions 3LNO during spring surveys from 1998-2002 and for fall surveys from 1995-2001. The highest numbers of shrimp were caught in the 2 o -4 o C-temperature range during the spring surveys with lower numbers in the 1 o -2 o C and 4 o -5 o C temperature ranges. During the fall surveys most shrimp were caught in the 1 o 3 o C temperature range. Cumulative frequency distribution of the number of shr imp caught and temperature indicates that only about 5% of the catches are associated with temperatures 4 o C during both spring and fall. Shrimp catches were mostly zero in all surveys in the swallow waters (<100 m) of the southeast Grand Bank, where temperatures generally range from 2 o -7 o C. In general, during the spring most of the large catches were found in the warmer water along the slopes of Div. 3LN, while in the fall, larger catches were found in most areas of Div. 3L including the inshore areas of the bays along the east coast of Newfoundland.

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 score0.112
Threshold uncertainty score0.222

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 head0.021
GPT teacher head0.242
Teacher spread0.222 · 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
Published2002
Admission routes1
Has abstractyes

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