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Record W2556689901 · doi:10.15273/pnsis.v43i2.3642

MARINE ECOSYSTEM ASSESSMENT: PAST, PRESENT AND FUTURE ATTEMPTS WITH EMPHASIS ON THE EASTERN SCOTIAN SHELF

2006· article· en· W2556689901 on OpenAlexaffvenue
Kenneth T. Frank, Jae S. Choi, Brian Petrie

Bibliographic record

VenueProceedings of the Nova Scotian Institute of Science · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsBedford Institute of Oceanography
Fundersnot available
KeywordsAbiotic componentFishingMarine ecosystemEcosystemGeographyFisheryFish stockAbundance (ecology)Biotic componentInvertebrateZooplanktonEcologyEnvironmental scienceOceanographyBiology

Abstract

fetched live from OpenAlex

The collapse of major fish stocks around the world, their failure to recover even after the cessation of fishing, and the perceived deficiencies in single species fisheries management has resulted in an intensified interest in the study of whole systems. Assessment of ecosystems is a relatively new phenomenon and represents a significant departure from the past focus on individual ecosystem components such as individual, commerciallyexploited stocks of fish and invertebrates in isolation from their physical, chemical and biological environment. A report entitled “State of the Eastern Scotian Shelf Ecosystem”was completed recently and some of its main findings form the body of this paper. The analysis focused on more than 60 data series, most extending back to at least 1970, associated with three categories of variables: biotic, abiotic and human. Biotic variables included the abundance, distribution and composition of finfish and invertebrates, phyto- and zooplankton, and marine mammals. Abiotic variables included oceanic and atmospheric data that specify ocean climate conditions. Human variables ranged from fisheries landings and revenue, activities associated with oil and gas development and contaminants. By examining temporal variations in the data, an assessment was made of the current status of the ecosystem relative to its past state.L’effondrement de stocks de poissons importants partout dans le monde, l’incapacité de ces stocks à se rétablir même après l’arrêt de toute activité de pêche et les lacunes perçues dans la gestion des pêches axées sur une espèce ont donné lieu à une hausse de l’intérêt pour les études écosystémiques. L’évaluation des écosystèmes est un domaine relativement nouveau et constitue un changement d’orientation considérable par rapport à l’importance accordée par le passé aux composantes individuelles des écosystèmes, comme les stocks de poissons et d’invertébrés particuliers exploités à des fins commerciales sans tenir compte de leur milieu physique, chimique et biologique. Un rapport intitulé « État de l’écosystème de l’est du plateau néo-écossais » a été parachevé récemment, et certaines des principales conclusions présentées dans ce rapport constituent le corps du présent document. L’analyse a porté sur trois catégories de facteurs (biotiques, abiotiques et anthropiques) dans plus de 60 jeux de données, la plupart remontant au minimum à 1970. Les variables biotiques sont notamment l’abondance et la répartition des poissons, des invertébrés, du phytoplancton, du zooplancton et des mammifères marins, ainsi que la composition de leurs communautés. Les variables abiotiques comprennent les données océaniques et atmosphériques qui permettent de comprendre le climat océanique. Les facteurs anthropiques englobent les prises de poisson, les revenus de pêche, les activités liées à la mise en valeur du pétrole et du gaz et les contaminants. L’examen des tendances temporelles dans les données a permis d’évaluer l’état actuel de l’écosystème par rapport à son état antérieur.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.489
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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

Citations1
Published2006
Admission routes2
Has abstractyes

Explore more

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