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Record W3009663011 · doi:10.1029/2019jg005425

Depth‐Resolved Photochemical Lability of Dissolved Organic Matter in the Western Tropical Pacific Ocean

2020· article· en· W3009663011 on OpenAlexaff
Fangming Yang, Guisheng Song, Philippe Massicotte, Hao Wei, Huixiang Xie

Bibliographic record

VenueJournal of Geophysical Research Biogeosciences · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité LavalUniversité du Québec à Rimouski
FundersNatural Science Foundation of Tianjin CityChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsColored dissolved organic matterDissolved organic carbonPhotobleachingLabilityEnvironmental chemistryDeep seaCarbon cycleOceanographyEnvironmental scienceSeawaterChemistryPhotochemistryGeologyFluorescencePhytoplanktonEcologyBiologyNutrientEcosystem

Abstract

fetched live from OpenAlex

Abstract Water samples collected from various depths of the offshore South China and Philippine Seas were exposed to solar‐simulated radiation. Photomineralization of dissolved organic carbon (DOC) and photobleaching of chromophoric dissolved organic matter (CDOM) and its humic‐like fluorescent constituent (FDOM) were observed in all samples. Protein‐like FDOM was, however, either photo‐decomposed or photo‐produced, depending on the sample's depth. The photobleaching of CDOM and humic‐like FDOM was much faster in deep than in shallow water samples while photomineralization displayed a weaker vertical zonation. Prior‐irradiated deep water inoculated with surface‐water bacteria showed enhanced microbial DOC removal but CDOM production. Results from this study suggest that deep‐ocean CDOM and FDOM can barely survive photobleaching during one ocean mixing cycle, but photochemical turnover of the bio‐refractory deep DOC is considerably longer than its average radiocarbon age. Coupled photochemical‐microbial processes can not only remove part of the bio‐refractory deep DOM but also regenerate part of it during ocean overturning circulation.

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)0.002
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 score0.007
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.282
Teacher spread0.244 · 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

Citations29
Published2020
Admission routes1
Has abstractyes

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