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Record W1499797361 · doi:10.1029/2011jg001930

Attributing carbon changes in conterminous U.S. forests to disturbance and non‐disturbance factors from 1901 to 2010

2012· article· en· W1499797361 on OpenAlexaff
Fangmin Zhang, Jing M. Chen, Yude Pan, Richard A. Birdsey, Shuanghe Shen, Weimin Ju, Liming He

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of ChinaU.S. Forest Service
KeywordsDisturbance (geology)Environmental scienceCarbon sinkSink (geography)Climate changeAtmospheric sciencesCarbon cycleEcosystemEcologyBiomass (ecology)GeographyBiologyGeology

Abstract

fetched live from OpenAlex

Recent climate variability (increasing temperature, droughts) and atmospheric composition changes (nitrogen deposition, rising CO 2 concentration) along with harvesting, wildfires, and insect infestations have had significant effects on U.S. forest carbon (C) uptake. In this study, we attribute C changes in the conterminous U.S. forests to disturbance and non‐disturbance factors with the help of forest inventory data, a continental stand age map, and an updated Integrated Terrestrial Ecosystem Carbon Cycle model (InTEC). We grouped factors into disturbances (harvesting, fire, insect infestation) and non‐disturbances (CO 2 concentration, N deposition, and climate variability) and estimated their subsequent impacts on forest regrowth patterns. Results showed that on average, the C sink in the conterminous U.S. forests from 1950 to 2010 was 206 Tg C yr −1 with 87% (180 Tg C yr −1 ) of the sink in living biomass. Compared with the simulation of all factors combined, the estimated C sink would be reduced by 95 Tg C yr −1 if disturbance factors were omitted, and reduced by 50 Tg C yr −1 if non‐disturbance factors were omitted. Our study also showed diverse regional patterns of C sinks related to the importance of driving factors. During 1980–2010, disturbance effects dominated the C changes in the South and Rocky Mountain regions, were almost equal to non‐disturbance effects in the North region, and had minor effects compared with non‐disturbance effects in the West Coast region.

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)0.001
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 score0.028
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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

Citations143
Published2012
Admission routes1
Has abstractyes

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