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Record W1976259623 · doi:10.1029/2012eo190007

Predicting carbon cycle feedbacks to climate: Integrating the right tools for the job

2012· article· en· W1976259623 on OpenAlex
Sharon Billings, Susan E. Ziegler, William H. Schlesinger, Ronald Benner, Daniel D. Richter

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEos · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCarbon cycleEnvironmental scienceCarbon fibersScale (ratio)EcosystemEarth system scienceClimate changeEarth scienceCarbon sequestrationTemporal scalesCarbon dioxideEnvironmental resource managementEcologyComputer scienceGeologyBiologyGeography

Abstract

fetched live from OpenAlex

Organic matter (OM) is a critical component of Earth's carbon cycle, with strong potential to provide positive feedbacks to a warmer climate via enhanced release of carbon dioxide. Investigations of the fate of OM typically focus on large reservoirs (e.g., soils) and on abundant, relatively long‐lived compounds (e.g., lignin, a compound derived from the cell walls of woody plants), and the products of its decay. Many of these investigations are challenged by issues of scale. The appropriate spatial scale of many carbon cycling questions requires infrastructure beyond the means of most projects and temporal scales often impossible to achieve in a human lifetime. We advocate coupling flux measurements, parameters that ecosystem scientists often quantify, with compositional characterization of sources, reservoirs, and sinks, data often generated by organic geochemists. Combined, the approaches of these disciplines offer powerful tools to understand OM dynamics.

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.

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 score0.171
Threshold uncertainty score0.325

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 communication0.0000.000
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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