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Record W2165660172 · doi:10.1139/f05-268

The influence of fall-spawning coho salmon (<i>Oncorhynchus kisutch</i>) on growth and production of juvenile coho salmon rearing in beaver ponds on the Copper River Delta, Alaska

2006· article· en· W2165660172 on OpenAlexvenueno aff
Dirk W. Lang, Gordon H. Reeves, James D. Hall, Mark S. Wipfli

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsOncorhynchusJuvenileFisheryBeaverDeltaBiologyFish <Actinopterygii>EcologyAnimal science

Abstract

fetched live from OpenAlex

This study examined the influence of fall-spawning coho salmon (Oncorhynchus kisutch) on the density, growth rate, body condition, and survival to outmigration of juvenile coho salmon on the Copper River Delta, Alaska, USA. During the fall of 1999 and 2000, fish rearing in beaver ponds that received spawning salmon were compared with fish from ponds that did not receive spawners and also with fish from ponds that were artificially enriched with salmon carcasses and eggs. The response to spawning salmon was variable. In some ponds, fall-spawning salmon increased growth rates and improved the condition of juvenile coho salmon. The enrichment with salmon carcasses and eggs significantly increased growth rates of fish in nonspawning ponds. However, there was little evidence that the short-term growth benefits observed in the fall led to greater overwinter growth or survival to outmigration when compared with fish from the nonspawning ponds. One potential reason for this result may be that nutrients from spawning salmon are widely distributed across the delta because of hydrologic connectivity and hyporheic flows. The relationship among spawning salmon, overwinter growth, and smolt production on the Copper River Delta does not appear to be limited entirely to a simple positive feedback loop.

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 score0.040
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.010
GPT teacher head0.187
Teacher spread0.177 · 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

Citations40
Published2006
Admission routes1
Has abstractyes

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