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Record W7098748711

1874-8392/11 2011 Bentham Open Open Access Changes in Seal Habitat Use of Nearshore Waters around Newfoundland and Southern Labrador: Implications for Potential Predation on Salmon

2016· article· en· W7098748711 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor Market and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPredationCapelinHabitatAbundance (ecology)Resource (disambiguation)Forage fishApex predator
DOInot available

Abstract

fetched live from OpenAlex

Abstract: The reasons for the decline in some Atlantic salmon (Salmo salar) stocks in Newfoundland and southern Labrador are not fully understood, but many resource users consider predation by seals in rivers and nearshore waters to be a contributing factor. To address these concerns, local ecological knowledge (LEK) interviews with resource users (n = 57) were conducted at 29 rivers throughout the Province to evaluate the potential for seal predation over a 25-year period when major changes were occurring in the structure of the Northwest Atlantic ecosystem. Based on LEK, eight rivers frequented by harp seals (Pagophilus groenlandicus), nine by harbor seals (Phoca vitulina) and three by gray seals (Halichoerus grypus) were evaluated as having a high potential for predation. According to respondents, the relative abundance of seals at these rivers started increasing in the mid 1990s or 2000, depending on the seal species involved. Variation in potential predation from river to river was attributed to a number of factors including the distribution of forage fish, variability in local ice conditions, the geography of the river and ecology of seal species frequenting the area. Resource users provided a useful and, in many cases, new perspective on the spatial and temporal overlap of seals, particularly harp seals, capelin (Mallotus villosus) and salmon in some areas. However, quantitative seal diet information and knowledge of seal-salmon relative abundances are required to assess the biological significance of these results from a salmon conservation perspective.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.807
Threshold uncertainty score0.650

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.1940.028

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 head0.118
GPT teacher head0.318
Teacher spread0.201 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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