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Carbon storage in successional and plantation forest soils: a tropical analysis

2012· article· en· W1932397188 on OpenAlex
E. Marín-Spiotta, Sapna Sharma

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Ecology and Biogeography · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsYork University
Fundersnot available
KeywordsEnvironmental scienceSoil waterSoil carbonTropicsTropical and subtropical dry broadleaf forestsAgroforestryForestryPrecipitationEcologyHumid subtropical climateSecondary forestGeographySoil scienceBiology

Abstract

fetched live from OpenAlex

Abstract Aim To analyse global patterns in soil carbon ( C ) in tropical successional and plantation forests based on climate, forest age, former land use and soil type to determine factors driving below‐ground C storage. Location Pantropical. Methods We conducted a synthesis of 81 studies reporting soil C stocks in more than 400 reforested and tree plantation sites. We used regression models and regression tree analyses to determine the importance of multiple predictor variables on soil C stocks standardized to three common depth ranges: 0–10, 0–30 and 0–100 cm. Results Mean annual temperature ( MAT ) was the most important predictor of soil C . Forest age explained little to no variability in soil C , in contrast with above‐ground studies. Data on long‐term trends in soil C are limited, as median time since forest growth was 15 years. Soil C stocks were similar between tropical secondary forests, tree plantations and reference forests. Differences between plantation and successional forests only appeared below 10 cm on sites with MAT < 21.3 ° C . Former pastures and cultivated sites differed from each other only to depths of 30 or 100 cm. Climatic variables appeared multiple times across all layers of the regression trees, consistent with strong interactions between MAT and precipitation on soil C stocks. Main conclusions Climate explained greater variability in soil C in successional and plantation forests than former land use or forest age, despite the tropical location of all sites. Human management factors were more important for predicting soil C stocks in cooler and drier sites, while environmental variables were more important in hotter and wetter sites. The relative importance and interactions between soil type, previous land use and forest cover type differed with soil depth, highlighting the importance of comparing C across consistent depths. Climatic controls suggest sensitivity of soil C stocks in successional and plantation forests to future climate change.

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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 score0.035
Threshold uncertainty score0.981

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 studies0.0000.000
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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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