MétaCan
Menu
Back to cohort

Using Noble Gases to Assess the Ocean's Carbon Pumps

2018· review· en· W2891515336 on OpenAlexafffund
Roberta C. Hamme, David Nicholson, William J. Jenkins, Steven Emerson

Bibliographic record

VenueAnnual Review of Marine Science · 2018
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsNeonEnvironmental scienceCarbon fibersNoble gasCarbon dioxideCarbon cycleDissolved organic carbonAtmospheric sciencesOceanographyArgonChemistryGeologyMaterials scienceEcology

Abstract

fetched live from OpenAlex

Natural mechanisms in the ocean, both physical and biological, concentrate carbon in the deep ocean, resulting in lower atmospheric carbon dioxide. The signals of these carbon pumps overlap to create the observed carbon distribution in the ocean, making the individual impact of each pump difficult to disentangle. Noble gases have the potential to directly quantify the physical carbon solubility pump and to indirectly improve estimates of the biological organic carbon pump. Noble gases are biologically inert, can be precisely measured, and span a range of physical properties. We present dissolved neon, argon, and krypton data spanning the Atlantic, Southern, Pacific, and Arctic Oceans. Comparisons between deep-ocean observations and models of varying complexity enable the rates of processes that control the carbon solubility pump to be quantified and thus provide an important metric for ocean model skill. Noble gases also provide a powerful means of assessing air-sea gas exchange parameterizations.

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)0.004
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
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 head0.072
GPT teacher head0.334
Teacher spread0.262 · 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 designOther design
Domainnot available
GenreReview

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

Citations34
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueAnnual Review of Marine ScienceSame topicMarine and coastal ecosystemsFrench-language works237,207