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Geographical differences in marine feeding of Atlantic salmon post‐smolts in Norwegian fjords

2004· article· en· W1501703668 on OpenAlexaff
Audun H. Rikardsen, Monika Haugland, P. A. Bjørn, Bengt Finstad, Rune Knudsen, J. Brian Dempson, Jens Christian Holst, Nils Arne Hvidsten, Marianne Holm

Bibliographic record

VenueJournal of Fish Biology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsFjordClupeaBiologyHerringSalmoFisheryPelagic zoneFish measurementPredationSpratEstuaryOceanographyEcologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Stomach content analyses were conducted on Atlantic salmon Salmo salar post‐smolt (average size, 119–154 mm fork length, L F ) caught in eight large Norwegian fjord systems along a north–south geographical axis during 1998–2001. In general, post‐smolts from southern Norway showed low feeding intensity in the fjords, whereas extensive feeding was observed in fjords in the northern and middle parts of Norway. The marine diet mainly included different crustaceans and in particular marine pelagic fish larvae (sand‐eels Ammodytes spp., herring Clupea harengus and gadoids), but with a substantial spatial and annual variation in prey diversity and feeding intensity. Insects were most frequently taken in the estuary, although fishes often made a large contribution in mass. In contrast, fishes, and to some extent various crustaceans (particularly Hyperiidae, Gammaridae, Euphausiacea and Copepoda) dominated the diet in the middle and outer parts of the fjords, where post‐smolts also fed more extensively than in the inner part. The results indicate that extensive feeding immediately after sea entrance may be more common for post‐smolts in the northern and middle parts of Norway, than in the southern fjords. The observed differences in post‐smolt feeding may be due to spatial and temporal differences in prey availability within and between the different types of fjord systems, and this might influence post‐smolt growth and survival.

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 score0.013
Threshold uncertainty score0.764

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 communication0.0000.000
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 head0.009
GPT teacher head0.219
Teacher spread0.211 · 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

Citations82
Published2004
Admission routes1
Has abstractyes

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