MétaCan
Menu
← Back to cohort
Record W6957957924 · doi:10.60692/zwtr0-nsp62

Impacts of ocean biogeochemistry on atmospheric chemistry

2023· article· en· W6957957924 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiogeochemistryAtmospheric chemistryAtmospheric compositionAerosolTrace gasTroposphereAtmosphere (unit)Tropospheric ozoneSink (geography)

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 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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.015
GPT teacher head0.185
Teacher spread0.170 · 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 designTheoretical or conceptual
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueGreater South Information System→Same topicAtmospheric chemistry and aerosols→French-language works237,207→