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Record W1970661490 · doi:10.1029/2012jc007909

Oxygen trends over five decades in the North Atlantic

2012· article· en· W1970661490 on OpenAlexaboutno aff
Ilaria Stendardo, Nicolas Gruber

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsIsopycnalOxygenOceanographyEnvironmental scienceGeologyChemistry

Abstract

fetched live from OpenAlex

We investigate long‐term trends in dissolved oxygen in the North Atlantic from 1960 to 2009 on the basis of a newly assembled high‐quality dataset consisting of oxygen data from three different sources: CARINA, GLODAP and the World Ocean Database. Oxygen trends are determined along isopycnal surfaces for eight regions and five water masses using a general least‐squares linear regression method that accounts for temporal auto‐correlation. Our results show a significant decrease of oxygen in the Upper (UW), Mode (MW) and Intermediate (IW) waters in almost all regions over the last 5 decades. Over the same period, oxygen increased in the Lower Intermediate Water (LIW) and Labrador Sea Water (LSW) throughout the North Atlantic. The observed oxygen decreases in the MW and IW of the northern and eastern regions are largely driven by changes in circulation and/or ventilation, while changes in solubility are the main driver for the oxygen decrease in the UW and the increases in the LIW and LSW. From 1960 until 2009 the UW, MW, and IW horizons have lost a total of −57 ± 34 Tmol, while the LIW and LSW horizons have gained 46 ± 47 Tmol, integrating to a roughly constant oxygen inventory in the North Atlantic. Comparing our oxygen trends with those of the oceanic heat content, we find an O 2 to heat change ratio of −3.6 ± 2.8 nmol J −1 for the UW, MW and IW, and a ratio of −2.8 ± 3.4 nmol J −1 for the LIW and LSW. These ratios are substantially larger than those expected from solubility alone.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.061
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.031
GPT teacher head0.296
Teacher spread0.265 · 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

Citations100
Published2012
Admission routes1
Has abstractyes

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