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Record W2164144714 · doi:10.5194/bg-9-1797-2012

Preformed and regenerated phosphate in ocean general circulation models: can right total concentrations be wrong?

2012· article· en· W2164144714 on OpenAlexafffund
Olaf Duteil, Wolfgang Koeve, Andreas Oschlies, Olivier Aumont, Daniele Bianchi, Laurent Bopp, Eric D. Galbraith, Richard J. Matear, J. Keith Moore, Jorge L. Sarmiento, Joachim Segschneider

Bibliographic record

VenueBiogeosciences · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité de MontréalMcGill University
FundersOffice of ScienceBundesministerium für Bildung und ForschungDeutsche ForschungsgemeinschaftEuropean CommissionCompute CanadaU.S. Department of EnergyU.S. Department of CommercePrinceton UniversityNational Oceanic and Atmospheric AdministrationFord Motor Company
KeywordsBiogeochemical cycleNutrientPhosphateEnvironmental scienceChemistryEnvironmental chemistryOceanographyBiological systemEcologyBiologyGeologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract. Phosphate distributions simulated by seven state-of-the-art biogeochemical ocean circulation models are evaluated against observations of global ocean nutrient distributions. The biogeochemical models exhibit different structural complexities, ranging from simple nutrient-restoring to multi-nutrient NPZD type models. We evaluate the simulations using the observed volume distribution of phosphate. The errors in these simulated volume class distributions are significantly larger when preformed phosphate (or regenerated phosphate) rather than total phosphate is considered. Our analysis reveals that models can achieve similarly good fits to observed total phosphate distributions for a~very different partitioning into preformed and regenerated nutrient components. This has implications for the strength and potential climate sensitivity of the simulated biological carbon pump. We suggest complementing the use of total nutrient distributions for assessing model skill by an evaluation of the respective preformed and regenerated nutrient components.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.203
Teacher spread0.183 · 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 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

Citations72
Published2012
Admission routes2
Has abstractyes

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