MétaCan
Menu
Back to cohort
Record W2227190018 · doi:10.1139/cjfas-2014-0524

Long-term stability in modelled zooplankton influx could uphold major fish spawning grounds on the Norwegian continental shelf

2015· article· en· W2227190018 on OpenAlexvenueno aff
Anders Frugård Opdal, Frode B. Vikebø

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersHavforskningsinstituttet
KeywordsCalanus finmarchicusGadusClupeaHerringZooplanktonOceanographyCalanusFisheryContinental shelfIchthyoplanktonDiel vertical migrationEnvironmental scienceBiologyFish <Actinopterygii>CrustaceanGeologyCopepod

Abstract

fetched live from OpenAlex

Early life stages of fish spawned on the Norwegian continental shelf have long been suggested to depend on eggs and nauplii from the crustacean zooplankton Calanus finmarchicus for survival. Calanus finmarchicus overwinters in the deep basins of the Norwegian Sea, and gravid females must be advected onto the shelf prior to spawning if eggs and nauplii larvae are to serve as food for fish larvae. In this study, cross-shelf advection of C. finmarchicus is simulated over 52 years (1960–2011) using a numerical ocean model coupled with an individual-based model. The results suggest that cross-shelf transport of C. finmarchicus is surprisingly stable across years and that transport is particularly concentrated immediately upstream of the two major spawning areas for the Northeast Arctic cod (Gadus morhua) and the Norwegian spring-spawning herring (Clupea harengus), namely Lofoten and Møre, respectively. Two large topographical features, the Træna Trough and the Norwegian Trench, appear to be funnelling C. finmarchicus onto the shelf in these two areas. This could suggest that the fish spawning grounds outside Møre and Lofoten are, in part, maintained owing to stable interannual food supply in spring.

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)0.001
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 score0.175
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.213
Teacher spread0.178 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicOceanographic and Atmospheric ProcessesFrench-language works237,207