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Record W2170312901 · doi:10.1126/science.1105959

Global Iron Connections Between Desert Dust, Ocean Biogeochemistry, and Climate

2005· review· en· W2170312901 on OpenAlexaff
T. D. Jickells, Zhisheng An, K. K. Andersen, Alex R. Baker, G. Bergametti, Nick Brooks, Junji Cao, Philip W. Boyd, Robert A. Duce, Keith A. Hunter, Hodaka Kawahata, N. Kubilay, Julie LaRoche, Peter S. Liss, N. M. Mahowald, Joseph M. Prospero, Andy Ridgwell, Ina Tegen, Rodrigo Torres

Bibliographic record

VenueScience · 2005
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiogeochemistryEarth system scienceEnvironmental scienceEarth scienceAtmosphere (unit)Iron fertilizationEarth (classical element)Global climateClimate changeAtmospheric sciencesClimatologyOceanographyEcologyMeteorologyGeologyGeographyNutrientBiology

Abstract

fetched live from OpenAlex

The environmental conditions of Earth, including the climate, are determined by physical, chemical, biological, and human interactions that transform and transport materials and energy. This is the "Earth system": a highly complex entity characterized by multiple nonlinear responses and thresholds, with linkages between disparate components. One important part of this system is the iron cycle, in which iron-containing soil dust is transported from land through the atmosphere to the oceans, affecting ocean biogeochemistry and hence having feedback effects on climate and dust production. Here we review the key components of this cycle, identifying critical uncertainties and priorities for future research.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.281
Teacher spread0.251 · 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 designNot applicable
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

Citations3,024
Published2005
Admission routes1
Has abstractyes

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