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Record W37249574 · doi:10.1111/jfb.15467

Pseudomonas in prei

2005· article· en· W37249574 on OpenAlexfundno aff
L. de Reijcke, L.S. van Overbeek

Bibliographic record

VenueProeftuinnieuws · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogenic Bacteria Studies
Canadian institutionsnot available
FundersLiber Ero Foundation
KeywordsPathovarPseudomonadaceaeBiologyPseudomonasBacteriaGenetics

Abstract

fetched live from OpenAlex

There is intense public interest surrounding the conservation and management of sharks, including a debate over whether sustainable shark fisheries are possible or fishing bans on sharks are needed to conserve these animals. An important but rarely discussed data point in discussions of global shark fisheries is the case of British Columbia's fishery for Pacific spiny dogfish, Squalus suckleyi, which in 2011 became the first Marine Stewardship Council-certified shark fishery anywhere in the world. A few years later, despite reportedly healthy local stocks and thriving global markets for this shark, the fishery voluntarily withdrew its MSC certification, and in recent years more than 95% of the quota for Pacific spiny dogfish has been left in the water. This study provides insight into what happened to this fishery through a review of grey literature and a series of stakeholder interviews with British Columbian fishermen, fish processors, managers and environmentalists. It is a rare case study of a fishery that largely ceased operations without a clear mechanistic explanation like a stock collapse, a government mandate to limit fishing or a clear shift in market demand. This fishery appears to have been affected by the combination of several factors, including a temporary reduction in biomass due to oceanographic effects, potential blowback from overly broad environmental messaging that did not distinguish between sustainable and unsustainable shark fisheries, management changes resulting in altered fishing incentives and changes to processing capacity associated with consolidating the fishing industry into ownership by relatively few large companies.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.335

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 head0.015
GPT teacher head0.200
Teacher spread0.185 · 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 designBench or experimental
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
Published2005
Admission routes1
Has abstractyes

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