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Record W3005413133 · doi:10.1002/lol2.10149

Organic sulfur: A spatially variable and understudied component of marine organic matter

2020· article· en· W3005413133 on OpenAlexaff
Krista Longnecker, Lisa Oswald, Melissa C. Kido Soule, Gregory A. Cutter, Elizabeth B. Kujawinski

Bibliographic record

VenueLimnology and Oceanography Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of Manitoba
FundersWoods Hole Oceanographic InstitutionGordon and Betty Moore FoundationNational Science Foundation
KeywordsSulfurOrganic matterEnvironmental chemistryTotal organic carbonSeawaterDissolved organic carbonCarbon fibersChemistryNitrogenHeteroatomSulfur cycleEnvironmental scienceOceanographyGeologyOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract Sulfur (S) is a major heteroatom in organic matter. This project evaluated spatial variability in the concentration and molecular‐level composition of organic sulfur along gradients of depth and latitude. We measured the concentration of total organic sulfur (TOS) directly from whole seawater. Our data reveal high variability in organic sulfur, relative to established variability in total organic carbon or nitrogen. The deep ocean contained significant amounts of organic sulfur, and the concentration of TOS in North Atlantic Deep Water (NADW) decreased with increasing age while total organic carbon remained stable. Analysis of dissolved organic matter extracts by ultrahigh resolution mass spectrometry revealed that 6% of elemental formulas contained sulfur. The sulfur‐containing compounds were structurally diverse, and showed higher numbers of sulfur‐containing elemental formulas as NADW moved southward. These measurements of organic sulfur in seawater provide the foundation needed to define the factors controlling organic sulfur in the global ocean.

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 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 score0.003
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.158
Teacher spread0.150 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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