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Record W2750618255 · doi:10.5670/oceanog.2017.234

Marine Mammals Exploring the Oceans Pole to Pole: A Review of the MEOP Consortium

2017· review· en· W2750618255 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOceanography · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
FundersBritish Antarctic SurveyUniversity of PretoriaNatural Environment Research CouncilMuséum National d'Histoire NaturelleDirectorate for Biological SciencesNorsk PolarinstituttCentre National de la Recherche ScientifiqueUniversity of St AndrewsUniversity of TasmaniaMacquarie UniversityUniversité de StrasbourgSight Research UK
KeywordsOceanographyMarine speciesMarine protected areaMarine researchEnvironmental resource managementGeographyEnvironmental scienceFisheryGeologyEcologyHabitatBiology

Abstract

fetched live from OpenAlex

Polar oceans are poorly monitored despite the important role they play in regulating Earths climate system. Marine mammals equipped with biologging devices are now being used to fill the data gaps in these logistically difficult to sample regions. Since 2002, instrumented animals have been generating exceptionally large data sets of oceanographic CTD casts (>500,000 profiles), which are now freely available to the scientific community through the MEOP data portal (http://meop.net). MEOP (Marine Mammals Exploring the Oceans Pole to Pole) is a consortium of international researchers dedicated to sharing animal-derived data and knowledge about the polar oceans. Collectively, MEOP demonstrates the power and cost-effectiveness of using marine mammals as data-collection platforms that can dramatically improve the ocean observing system for biological and physical oceanographers. Here, we review the MEOP program and database to bring it to the attention of the international community.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0030.005
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.097
GPT teacher head0.319
Teacher spread0.222 · 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