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Record W4386598118 · doi:10.1525/elementa.2023.00032

Impacts of ocean biogeochemistry on atmospheric chemistry

2023· article· en· W4386598118 on OpenAlexaff
Liselotte Tinel, Jonathan P. D. Abbatt, E. S. Saltzman, Anja Engel, Rafael P. Fernández, Qinyi Li, Anoop S. Mahajan, M. R. Nicewonger, Gordon A. Novak, Alfonso Saiz‐Lopez, Stephanie R. Schneider, Shanshan Wang

Bibliographic record

VenueElementa Science of the Anthropocene · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersNational Science Foundation
KeywordsBiogeochemistryAtmospheric chemistryEnvironmental scienceTrace gasAtmosphere (unit)OzoneAir quality indexAerosolTroposphereAtmospheric sciencesStratosphereEarth scienceChemistryEnvironmental chemistryMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Ocean biogeochemistry involves the production and consumption of an array of organic compounds and halogenated trace gases that influence the composition and reactivity of the atmosphere, air quality, and the climate system. Some of these molecules affect tropospheric ozone and secondary aerosol formation and impact the atmospheric oxidation capacity on both regional and global scales. Other emissions undergo transport to the stratosphere, where they contribute to the halogen burden and influence ozone. The oceans also comprise a major sink for highly soluble or reactive atmospheric gases. These issues are an active area of research by the SOLAS (Surface Ocean Lower Atmosphere) community. This article provides a status report on progress over the past decade, unresolved issues, and future research directions to understand the influence of ocean biogeochemistry on gas-phase atmospheric chemistry. Common challenges across the subject area involve establishing the role that biology plays in controlling the emissions of gases to the atmosphere and the inclusion of such complex processes, for example involving the sea surface microlayer, in large-scale global models.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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 designBench or experimental
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

Citations11
Published2023
Admission routes1
Has abstractyes

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