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Record W7130728160 · doi:10.5282/rcc/10067

Sustainabiltiy in Fish Farming: Global and Local Challenges and Opportunities

2012· article· en· W7130728160 on OpenAlexaboutno aff
Otto Andreassen

Bibliographic record

VenueUniversitätsbibliothek der LMU · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityNorwegianFish <Actinopterygii>AgricultureFocus (optics)Environmental sociology

Abstract

fetched live from OpenAlex

In his discourse, focusing primarily on Norwegian salmon farming, Andreassen endeavors to foster a comparative dialogue with Canadian practices within a sustainability framework. Reflecting on the profound impact of agriculture throughout history and the escalating demand for seafood, he underscores the imperative of sustainability. Utilizing a framework comprising environmental, economic, social, and institutional dimensions, Andreassen lauds Norway's environmental focus while advocating for broader considerations, particularly in social and economic spheres. Emphasizing the significance of collaborative efforts, he suggests Integrated Coastal Zone Management as a means to address multifaceted challenges and achieve sustainability in the salmon-farming industry.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.085
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.025
Scholarly communication0.0100.006
Open science0.0010.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.239
Teacher spread0.210 · 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
Published2012
Admission routes1
Has abstractyes

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