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Tropical pasture carbon cycling: relationships between C source/sink strength, above‐ground biomass and grazing

2002· article· en· W2098650614 on OpenAlexaff
Brian J. Wilsey, Gabrielle Parent, Nigel T. Roulet, Tim R. Moore, Catherine Potvin

Bibliographic record

VenueEcology Letters · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsGrazingPastureEcosystemBiomass (ecology)Environmental scienceStanding cropEcosystem respirationAgronomyCyclingCanopyCarbon sinkCarbon cycleProductivitySink (geography)EcologyAnimal scienceBiologyPrimary productionForestryGeography

Abstract

fetched live from OpenAlex

Abstract We measured net ecosystem CO2 exchange (NEE) in Panamá over C4 pasture plots that varied in grazing intensity. After adjusting for variation in light, there were noticeable effects of grazing‐related variables on CO2 exchange that were largely dependent on the developmental stage of the plant canopy. Above‐ground productivity was positively related to grazing intensity (r2=0.30). Two experimentally grazed fields had significantly lower standing crop biomass but no significant difference in CO2 uptake (24.2 μmol/m2/s) compared with two ungrazed fields (20.3 μmol/m2/s). Grazed fields had significantly lower ecosystem respiration rates (10.3 μmol/m2/s) than did ungrazed fields (17.6 μmol/m2/s). These results suggest that, although these pastures were possible sources of CO2 during the time intervals sampled, the size of the sources tended to be dampened by cattle grazing through reductions in ecosystem respiration. Thus, it appears that disturbance caused by cattle grazing will not always result in an increase in CO2 release from tropical pastures to the atmosphere.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.014
GPT teacher head0.191
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), 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

Citations80
Published2002
Admission routes1
Has abstractyes

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