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Partial pressure (or fugacity) of carbon dioxide and other variables collected from Surface underway observations using Carbon dioxide (CO2) gas analyzer and other instruments from NUKA ARCTICA in the Davis Strait, Kattegat and others from 2009-01-14 to 2009-12-30 (NCEI Accession 0157378)

2016· dataset· en· W6946187102 on OpenAlexaboutno aff

Bibliographic record

VenueNational Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) · 2016
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicBanana Cultivation and Research
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon dioxidePartial pressureNorth seaCruiseSea surface temperatureGas analysisSurface water

Abstract

fetched live from OpenAlex

This dataset includes Surface underway, chemical and physical data collected from NUKA ARCTICA in the Davis Strait, Kattegat, The Sound, Great Belt, Little Belt, Labrador Sea, North Atlantic Ocean, North Sea, Norwegian Sea and Skagerrak from 2009-01-14 to 2009-12-30. These data include Partial pressure (or fugacity) of carbon dioxide - atmosphere, Partial pressure (or fugacity) of carbon dioxide - water and SEA SURFACE TEMPERATURE. The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer. These data were collected by Truls Johannessen of Bjerknes Centre for Climate Research, Are Olsen of University of Bergen; Geophysical Institute (GFI), and Sara Jutterström of University of Gothenburg (GU) as part of the VOS_Nuka_Arctica_2009 dataset. CDIAC associated the following cruise ID(s) with this dataset: 26NA20090114

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.027

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 head0.041
GPT teacher head0.267
Teacher spread0.225 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2016
Admission routes1
Has abstractyes

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