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Record W2067133458 · doi:10.1029/2008gl034795

Natural abundance measurements of <sup>13</sup>C indicate increased deep soil carbon mineralization after forest disturbance

2008· article· en· W2067133458 on OpenAlexaff
Amanda Diochon, Lisa Kellman

Bibliographic record

VenueGeophysical Research Letters · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsSt. Francis Xavier UniversityDalhousie University
Fundersnot available
KeywordsChronosequenceSoil carbonEnvironmental scienceEcosystemHumusDisturbance (geology)Mineralization (soil science)Soil waterAbundance (ecology)Soil scienceForest ecologySoil horizonBlue carbonEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Northern forest soils represent globally important stores of carbon (C), yet there is no consensus about how they are altered by the widespread practice of harvesting that dominates many forested landscapes. Here we present the first study to systematically investigate the utility of δ 13 C and C content depth profiles to infer temporal changes in belowground carbon cycling processes following disturbance in a pure C 3 ecosystem. We document carbon concentration and δ 13 C depth profile enrichment trends consistent with a kinetic fractionation arising from soil organic carbon (SOC) humification across a northern forest chronosequence (1, 15, 45, 80 and 125+ yrs). Reduced soil C storage that coincided with observed soil profile δ 13 C‐enrichment patterns which intensified following clearcut harvesting, pointed to losses of SOC in the deeper (&gt;20 cm) mineral soil. This study suggests the δ 13 C approach may assist in identifying mechanisms responsible for soil C storage changes in disturbed C 3 forest ecosystems.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.031
GPT teacher head0.252
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.

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

Citations85
Published2008
Admission routes1
Has abstractyes

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