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Record W1541740378 · doi:10.1029/2006gb002879

A high‐resolution survey of DMS, CO<sub>2</sub>, and O<sub>2</sub>/Ar distributions in productive coastal waters

2008· article· en· W1541740378 on OpenAlexaffabout
Nina Nemcek, Debby Ianson, Philippe D. Tortell

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsFisheries and Oceans CanadaUniversity of British Columbia
Fundersnot available
KeywordsHydrographySampling (signal processing)Environmental scienceChlorophyll aResolution (logic)Atmospheric sciencesSpatial variabilityImage resolutionOceanographyChemistryGeologyPhysicsStatisticsMathematics

Abstract

fetched live from OpenAlex

We present continuous, high‐resolution measurements of surface dimethylsulfide (DMS), p CO 2 , and O 2 /Ar obtained in coastal waters off British Columbia, Canada, using membrane inlet mass spectrometry (MIMS). Sampled underway at a frequency of twice per minute (every ∼160 m at 10 knots cruising speed), our data reveal fine‐scale structure in gas variability and its covariance with a number of hydrographic parameters. All parameters exhibited large ranges ( p CO 2 , 200–747 ppm; DMS, &lt;1–29 nM; chl a , &lt;0.1–33 μ g L −1 ), highlighting the dynamic nature of the study area. A strong anticorrelation between p CO 2 and O 2 /Ar was observed across the survey region, with the distributions of these gases influenced by biology and its interplay with physical processes. In contrast, DMS levels, which varied dramatically over short distances, showed no significant correlations with any single variable for the full, high‐resolution data set. However, when measurements were binned to a much coarser spatial resolution, we found a linear relationship between surface DMS and the chlorophyll/mixed layer depth ratio. The slope of this relationship differed significantly from that previously derived from open ocean data. We used several statistical techniques to estimate the spatial variability of gases and hydrographic parameters and the inherent sampling errors associated with low‐frequency sampling approaches. These analyses emphasize the importance of high‐resolution sampling in coastal areas, particularly for DMS.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.210
Teacher spread0.198 · 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

Citations61
Published2008
Admission routes2
Has abstractyes

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