MétaCan
Menu
Back to cohort
Record W2029517592 · doi:10.1139/x02-020

Foliar litter position and decomposition in a fire-maintained longleaf pine wiregrass ecosystem

2002· article· en· W2029517592 on OpenAlexvenueno aff
Joseph J. Hendricks, Carlos A Wilson, Lindsay R. Boring

Bibliographic record

VenueCanadian Journal of Forest Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersWoodruff FoundationNational Science Foundation
KeywordsLitterAnimal scienceNutrientPinus <genus>DecompositionBiologyEcosystemBiomass (ecology)BotanyAgronomyEcology

Abstract

fetched live from OpenAlex

Foliar litter position and decomposition were assessed in longleaf pine (Pinus palustris Mill.) - wiregrass (Aristida beyrichiana Trin. &amp; Rupr.) woodlands during a 3-year burn interval. Position assessments revealed 57.7 and 67.4% of foliar litter was elevated in wiregrass crowns 1 and 2 years, respectively, following burning. Decomposition assessments revealed soil-surface mass loss decay constants (range 0.097–0.282) similar to those measured in comparable pine forests. However, elevated longleaf pine and wiregrass litter exhibited decay constants (0.052 and 0.074, respectively) 50% lower than corresponding soil-surface rates and among the lowest values in the literature. With the exception of wiregrass, which did not exhibit an immobilization of the nutrients (N, P, Ca, K, and Mg) assessed, foliar litter exhibited either extensive P immobilization with minimal N immobilization or minimal, short-lived immobilization of N, P, or both N and P. The percentage of original N and P remaining after 3 years varied widely among the soil surface (N range 6.3–56.3%; P range 3.4–204.7%) and elevated (N range 76.8–94.9%; P range 52.0–99.2%) litter. These results suggest that fire regimes typically employed in longleaf pine – wiregrass woodlands may balance N losses via volatilization with P limitations via litter immobilization.

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)0.002
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 score0.360
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.017
GPT teacher head0.265
Teacher spread0.248 · 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

Citations64
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicFire effects on ecosystemsFrench-language works237,207