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Record W2042419248 · doi:10.5194/essd-6-91-2014

Measurements of the dissolved inorganic carbon system and associated biogeochemical parameters in the Canadian Arctic, 1974–2009

2014· article· en· W2042419248 on OpenAlexaffabout
Karina E. Giesbrecht, Lisa A. Miller, Marty Davelaar, Sarah Zimmermann, Eddy C. Carmack, William K. Johnson, Robie W. Macdonald, F. A. McLaughlin, Alfonso Mucci, William J. Williams, C. S. Wong, Michiyo Yamamoto‐Kawai

Bibliographic record

VenueEarth system science data · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsMcGill UniversityUniversity of VictoriaFisheries and Oceans Canada
Fundersnot available
KeywordsArcticBiogeochemical cycleEnvironmental scienceSubarctic climateOceanographyAlkalinityTotal inorganic carbonDissolved organic carbonThe arcticCarbon fibersCarbon dioxideGeologyEnvironmental chemistryChemistry

Abstract

fetched live from OpenAlex

Abstract. We have assembled and conducted primary quality control on previously publicly unavailable water column measurements of the dissolved inorganic carbon system and associated biogeochemical parameters (oxygen, nutrients, etc.) made on 26 cruises in the subarctic and Arctic regions dating back to 1974. The measurements are primarily from the western side of the Canadian Arctic, but also include data that cover an area ranging from the North Pacific to the Gulf of St. Lawrence. The data were subjected to primary quality control (QC) to identify outliers and obvious errors. This data set incorporates over four thousand individual measurements of total inorganic carbon (TIC), alkalinity, and pH from the Canadian Arctic over a period of more than 30 years and provides an opportunity to increase our understanding of temporal changes in the inorganic carbon system in northern waters and the Arctic Ocean. The data set is available for download on the CDIAC (Carbon Dioxide Information Analysis Center) website: http://cdiac.ornl.gov/ftp/oceans/IOS_Arctic_Database/ (doi:10.3334/CDIAC/OTG.IOS_ARCT_CARBN).

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)0.005
metaresearch head score (Gemma)0.001
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 score0.207
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.035
GPT teacher head0.222
Teacher spread0.187 · 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

Citations8
Published2014
Admission routes2
Has abstractyes

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