MétaCan
Menu
Back to cohort
Record W2025343504 · doi:10.1029/2009gl040423

Evidence of O<sub>2</sub> consumption in underway seawater lines: Implications for air‐sea O<sub>2</sub> and CO<sub>2</sub> fluxes

2010· article· en· W2025343504 on OpenAlexaff
L. W. Juranek, Roberta C. Hamme, Jan Kaiser, Rik Wanninkhof, Paul D. Quay

Bibliographic record

VenueGeophysical Research Letters · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSeawaterEnvironmental scienceEnvironmental chemistryOceanographyAtmospheric sciencesChemistryGeology

Abstract

fetched live from OpenAlex

We observed O2 deficits of 0.5 to 2.0% (1 to 4 μmol/kg) in the underway seawater lines of three different ships. Deficits in O2/Ar and isotopic enrichments in dissolved O2 observed in underway seawater lines indicate a respiratory removal process. A 1% respiratory bias in underway lines would lead to a 2.5–5 μatm (2.5–5 μbar) enhancement in surface water pCO2. If an underway pCO2 bias of this magnitude affected all measurements, the global oceanic carbon uptake based on pCO2 climatologies would be 0.5–0.8 Pg/yr higher than the present estimate of 1.6 Pg/yr. Treatment of underway lines with bleach for several hours and thorough flushing appeared to minimize O2 loss. Given the increasing interest in underway seawater measurements for the determination of surface CO2 and O2 fluxes, respiration in underway seawater lines must be identified and eliminated on all observing ships to ensure unbiased data.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.302
Teacher spread0.254 · 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 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

Citations50
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicMarine and coastal ecosystemsFrench-language works237,207