MétaCan
Menu
Back to cohort
Record W1995747260 · doi:10.5194/bgd-3-1763-2006

Significant long-term increase of fossil fuel CO <sub>2</sub> uptake from reduced marine calcification

2006· preprint· en· W1995747260 on OpenAlexafffund
Andy Ridgwell, Ingrid Zondervan, J. C. Hargreaves, Jelle Bijma, Timothy M. Lenton

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of British Columbia
FundersCanada Research ChairsJapan Agency for Marine-Earth Science and TechnologyNatural Environment Research CouncilCanadian Foundation for Climate and Atmospheric Sciences
KeywordsEmiliania huxleyiPlanktonCalcificationOceanographyEnvironmental scienceRange (aeronautics)CoccolithophorePhytoplanktonOcean acidificationCarbonateAtmospheric sciencesChemistryEcologySeawaterGeologyBiologyMaterials scienceNutrientInternal medicine

Abstract

fetched live from OpenAlex

Abstract. Analysis of available plankton manipulation experiments demonstrates a previously unrecognized wide range of sensitivities of biogenic calcification to simulated anthropogenic acidification of the ocean, with the "lab rat" of planktic calcifiers, Emiliania huxleyi not representative of calcification generally. We assess the implications of the experimental uncertainty in plankton calcification response by creating an ensemble of realizations of an Earth system model that encapsulates a comparable range of uncertainty in calcification response. We predict a substantial future reduction in marine carbonate production, with ocean CO2 sequestration across the model ensemble enhanced by between 62 and 199 PgC by the year 3000, equivalent to a reduction in the atmospheric fossil fuel CO2 burden at that time of up to 13%. Concurrent changes in ocean circulation and surface temperatures contribute about one third to the overall importance of reduced plankton calcification.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.239
Teacher spread0.221 · 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.

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

Citations9
Published2006
Admission routes2
Has abstractyes

Explore more

Same topicOcean Acidification Effects and ResponsesFrench-language works237,207