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Record W2010412604 · doi:10.1029/2009eo240005

Workshop on Marine Research Drilling: Cold‐Water Carbonate Reservoir Systems in Deep Environments (COCARDE): A Pilot Industry‐Academia Partnership in Marine Research Drilling; Fribourg, Switzerland, 21–24 January 2009

2009· article· en· W2010412604 on OpenAlexaboutno aff
Silvia Spezzaferri

Bibliographic record

VenueEos · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOceanographyCarbonateMarine researchEuropean unionGeneral partnershipCoral reefMarine ecosystemEcosystemEarth scienceGeologyEcologyPolitical scienceBusiness

Abstract

fetched live from OpenAlex

Cold‐water carbonate mounds supporting cold‐water coral ecosystems, often dominated by Lophelia pertusa and Madrepora oculata , are widespread along the Atlantic margins from Norway to Mauritania. During the past 10 years, the scientific community has accumulated new insights on their occurrence and development and identified their potential role in reservoir formation, thus establishing a framework for collaboration with the hydrocarbon industry. A Magellan workshop, sponsored by the European Science Foundation (ESF; http://www.esf.org/), was held in Switzerland in January. The workshop gathered 35 scientists from 10 European and two extra‐European countries (Canada and Morocco), representing 20 research teams, including members of two Integrated Ocean Drilling Program (IODP) proposals. Some of the participants were also involved with two ESF European Collaborative Research (EUROCORES) projects [Microbial Diversity and Functionality in Cold‐Water Coral Reef Ecosystems (MiCROSYSTEMS) and Mid‐Latitude Carbonate Systems: Complete Sequences From Cold‐Water Coral Carbonate Mounds in the Northeast Atlantic (CARBONATE)], and the European Union Framework Program 6 integrated project Hotspot Ecosystem Research on the Margins of European Seas (HERMES).

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0000.001

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 head0.103
GPT teacher head0.337
Teacher spread0.234 · 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.

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
Published2009
Admission routes1
Has abstractyes

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