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Record W2124552688 · doi:10.1093/icesjms/fsn193

Natura 2000 sites and fisheries in German offshore waters

2008· article· en· W2124552688 on OpenAlexaff
Søren Anker Pedersen, Heino O. Fock, Jochen C. Krause, Christian Pusch, Anne Sell, Uwe Böttcher, S.I. Rogers, Mattias Sköld, Henrik Skov, Magdalena Podolska, G.J. Piet, Jake Rice

Bibliographic record

VenueICES Journal of Marine Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsGovernment of CanadaFisheries and Oceans Canada
Fundersnot available
KeywordsNatura 2000HabitatFishingMarine protected areaFisheryGeographyGermanHabitats DirectiveWildlife conservationEnvironmental resource managementEnvironmental protectionEcologyEnvironmental scienceArchaeologyBiology

Abstract

fetched live from OpenAlex

Abstract Pedersen, S. A., Fock, H., Krause, J., Pusch, C., Sell, A. L., Böttcher, U., Rogers, S. I., Sköld, M., Skov, H., Podolska, M., Piet, G. J., and Rice, J. C. 2009. Natura 2000 sites and fisheries in German offshore waters.–ICES Journal of Marine Science, 66: 155–169. The principal objective of sites selected as part of Natura 2000 is to achieve or maintain a favourable conservation status of habitats and species named in the EU Birds and Habitats directives. In the German exclusive economic zone, the habitat types protected by this legislation are sandbanks and reefs; protected species include marine mammals, seabirds, and specific migratory fish species. The ICES project Environmentally Sound Fishery Management in Protected Areas (EMPAS) aims to answer two questions: (i) To what extent do specific fishing activities significantly threaten attainment of the conservation objectives of the Natura 2000 sites? (ii) What management measures would reduce these conflicts and how effective would they be at helping to ensure the favourable condition of these sites? Assessments of fishing impacts on Natura 2000 sites require basic data on the conservation status of individual habitats and species, as well as data for fine-scale distributions of ongoing fishing activities. This paper describes and discusses the process used by the EMPAS project in developing fishery-management plans for each Natura 2000 site in German offshore waters.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.501

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.003
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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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 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

Citations53
Published2008
Admission routes1
Has abstractyes

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