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Record W2110186991 · doi:10.1111/ele.12075

Convergence of potential net ecosystem production among contrasting C<sub>3</sub>grasslands

2013· article· en· W2110186991 on OpenAlexaff
Matthias Peichl, Oliver Sonnentag, Georg Wohlfahrt, Lawrence B. Flanagan, Dennis Baldocchi, Gerard Kiely, Marta Galvagno, Damiano Gianelle, Barbara Marcolla, Casimiro Pio, Mirco Migliavacca, Michael B. Jones, Matthew Saunders

Bibliographic record

VenueEcology Letters · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of LethbridgeUniversité de Montréal
FundersTiroler WissenschaftsförderungAustrian Science Fund
KeywordsPrimary productionEcosystemEddy covarianceEcosystem respirationEnvironmental scienceBiomass (ecology)EcologyTerrestrial ecosystemClimate changeRange (aeronautics)Atmospheric sciencesBiology

Abstract

fetched live from OpenAlex

Metabolic theory and body size constraints on biomass production and decomposition suggest that differences in the intrinsic potential net ecosystem production (NEPPOT ) should be small among contrasting C3 grasslands and therefore unable to explain the wide range in the annual apparent net ecosystem production (NEPAPP ) reported by previous studies. We estimated NEPPOT for nine C3 grasslands under contrasting climate and management regimes using multiyear eddy covariance data. NEPPOT converged within a narrow range, suggesting little difference in the net carbon dioxide uptake capacity among C3 grasslands. Our results indicate a unique feature of C3 grasslands compared with other terrestrial ecosystems and suggest a state of stability in NEPPOT due to tightly coupled production and respiration processes. Consequently, the annual NEPAPP of C3 grasslands is primarily a function of seasonal and short-term environmental and management constraints, and therefore especially susceptible to changes in future climate patterns and associated adaptation of management practices.

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 score0.458
Threshold uncertainty score0.364

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 head0.002
GPT teacher head0.149
Teacher spread0.146 · 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

Citations25
Published2013
Admission routes1
Has abstractyes

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