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Record W2396787737 · doi:10.1139/cjfas-2015-0450

The response of North Atlantic diadromous fish to multiple stressors, including land use change: a multidecadal study

2016· article· en· W2396787737 on OpenAlexvenueno aff
Elvira de Eyto, Catherine M. Dalton, Mary Dillane, Eleanor Jennings, Philip McGinnity, Barry O’Dwyer, Russell Poole, Ger Rogan, David Taylor

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFish migrationSalmoFisheryNorth Atlantic oscillationHatcheryClimate changeHabitatTroutBrown troutEnvironmental scienceEcologyBiologyGeographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Reduction of freshwater habitat quality due to land use change can have major impacts on diadromous fish. Partitioning this impact from other potential drivers, such as changing marine conditions and climate, is hampered by a lack of long-term data sets. Here, four decades of data were used to assess the impact of land use change on Salmo salar and anadromous Salmo trutta in the Burrishoole catchment, Ireland, one of the few index sites for diadromous fish in the North Atlantic. Land use change was found to have no significant impact on the freshwater survival of either salmon or trout. However, climate impacted significantly on the survival of salmon and trout in fresh water, with poor survival in years with wetter, warmer winters, coinciding with positive North Atlantic Oscillation values. Additionally, cold springs were associated with higher survival in trout. The addition of hatchery fish into the salmon spawning cohort coincided with low freshwater survival. Our results highlight the necessity for a broad ecosystem approach in any conservation effort of these species.

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 score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.044
GPT teacher head0.243
Teacher spread0.199 · 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

Citations41
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→