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Record W1867297214 · doi:10.1029/2001gb001831

Soil CO<sub>2</sub> production and surface flux at four climate observatories in eastern Canada

2002· article· en· W1867297214 on OpenAlexaffabout
David Risk, Lisa Kellman, Hugo Beltrami

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsEnvironmental scienceWater contentSoil waterContext (archaeology)Flux (metallurgy)Soil scienceSoil horizonSubsurface flowThermal diffusivityCarbon dioxideMoistureAtmosphere (unit)Atmospheric sciencesHydrology (agriculture)GroundwaterGeologyChemistryGeographyMeteorology

Abstract

fetched live from OpenAlex

Soils constitute the largest terrestrial source of carbon dioxide to the atmosphere, and in the context of changing global temperature and moisture patterns, it is critical that we understand the climatic controls on soil respiration. We use subsurface CO 2 concentrations, surface CO 2 flux and detailed physical monitoring of the subsurface regime to examine physical controls on soil CO 2 production. Results indicate that subsurface CO 2 production is very sensitive to the subsurface thermal regime, where relationships were robust and also stable across all land use types studied. In contrast, the thermal dependence of surface CO 2 flux was much weaker. We found that soil heat content, rather than soil temperature, was the most descriptive index of the biological processes contributing to soil profile CO 2 production at our study sites. Soil moisture was also found to have an important influence on subsurface CO 2 production, particularly because of the relationship between moisture and soil profile diffusivity. Nondiffusive profile CO 2 transport also appears to be important at these sites where the subsurface controls on transport change regularly and markedly.

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 score0.128
Threshold uncertainty score0.945

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 head0.010
GPT teacher head0.188
Teacher spread0.178 · 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

Citations63
Published2002
Admission routes2
Has abstractyes

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