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Record W4311963837 · doi:10.7185/geochemlet.2242

High latitude controls on dissolved barium isotope distributions in the global ocean

2022· article· en· W4311963837 on OpenAlexafffund
Yang Yu, R.C. Xie, M. Gutjahr, G. Laukert, Zhonghui CAO, E. Hathorne, C. Siebert, G. Patton, M. Frank

Bibliographic record

VenueGeochemical Perspectives Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsFisheries and Oceans CanadaDalhousie University
FundersCanada First Research Excellence FundOcean Frontier InstituteChina Scholarship CouncilDeutsche Forschungsgemeinschaft
KeywordsBariumLatitudeIsotopeOceanographyEnvironmental scienceGeologyChemistryGeodesyInorganic chemistryPhysicsNuclear physics

Abstract

fetched live from OpenAlex

The high latitude regions play a key role in regulating the marine biogeochemical cycling of barium (Ba) and the pre-formed Ba isotope compositions in the global ocean. In this study, we present 17 new depth profiles of dissolved Ba concentrations ([Ba]) and isotope compositions ( 138 Ba) from the high latitude Atlantic, Pacific and Southern Oceans to trace the ventilation of deep waters in the Southern Ocean and their subsequent transport throughout the global ocean. Our data reveal how biogeochemical processes in the Southern Ocean generate distinct 138 Ba signatures of upper ocean water masses, and that large scale ocean circulation constrains the meridional gradient of 138 Ba distributions in the deep Atlantic Ocean. The significant increase in [Ba] of deep waters in the North Pacific is mainly achieved through dissolution of sinking particles which adds a 138 Ba signal comparable to the deep Pacific 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 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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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 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

Citations14
Published2022
Admission routes2
Has abstractyes

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