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Record W2085192680 · doi:10.1139/f08-191

Summer microhabitat partitioning by different size classes of masu salmon (Oncorhynchus masou) in habitats formed by installed large wood in a large lowland river

2009· article· en· W2085192680 on OpenAlexvenueno aff
Shigeya Nagayama, Yôichi Kawaguchi, Daisuke Nakano, Futoshi Nakamura

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsOncorhynchusHabitatRiparian zoneAbundance (ecology)FisheryEnvironmental scienceCurrent (fluid)EcologyFish <Actinopterygii>BiologyHydrology (agriculture)GeologyOceanography

Abstract

fetched live from OpenAlex

Different size classes of masu salmon ( Oncorhynchus masou ) were partitioned in three-dimensional space in habitats created by artificially installed large wood (LW) structures in a large river. Fish &gt;300 mm in size (L-sized) returning from the ocean distinctly occurred in sheltered areas near the riverbed, which had a moderate current velocity and contained large root wads or tree trunks; 140–200 mm (M-sized) and 100–120 mm (S-sized) fish selected deep areas of high velocity current adjacent to LW structures; ≤80 mm fish (SS-sized) were most common in the lower depth layers throughout all LW habitats, including shallow areas with moderate currents where LW structures blocked the fast currents. Some SS-sized fish used cover areas provided by branches and leaves. Masu salmon abundance in all size classes combined was greater in habitats with LW structures than in habitats without them. Our study suggests that the natural recruitment of whole trees from the riparian zone or artificial placement of whole trees will have a profound effect on creating salmonid habitats in large rivers.

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 score0.095
Threshold uncertainty score0.920

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 communication0.0000.001
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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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

Citations34
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicHydrology and Sediment Transport ProcessesFrench-language works237,207