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Record W2523965477 · doi:10.1139/cjfr-2016-0147

Variability in aboveground carbon driven by slope aspect and curvature in an eastern deciduous forest, USA

2016· article· en· W2523965477 on OpenAlexvenueno aff
Lauren A. Smith, David M. Eissenstat, Margot W. Kaye

Bibliographic record

VenueCanadian Journal of Forest Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsDeciduousEnvironmental scienceBiomass (ecology)LitterCanopyForest floorSpatial variabilityPlant litterEcosystemEcologySwaleWatershedHydrology (agriculture)Surface runoffSoil scienceSoil waterBiologyGeology

Abstract

fetched live from OpenAlex

In forested ecosystems, topography and tree species contribute to spatial variability in carbon (C) dynamics through differential rates of C uptake and storage; therefore, estimates of species-specific and spatial variability in C can strengthen ecosystem budgets. To produce such estimates, we deconstructed watershed-scale C and component pools (e.g., wood biomass, litter) and fluxes at a fine scale using a small mixed deciduous forest catchment to determine the variation due to topographic position and species. Factors affecting fluxes included aspect, slope curvature, tree species contributions, and litter production. Annual C uptake into wood varied across the catchment from 0 to 34 kg C·year −1 and was 20% greater on southern aspects than northern ones and 33% greater in swales than non-swale slopes. Of the more than 20 tree species found in the forest canopy of the catchment, highest C uptake in woody biomass was measured in Quercus rubra L. growing in swales, followed by Quercus prinus L. syn. growing on the southern aspect, with the lowest aboveground wood increment measured in Pinus at higher elevations on non-swale slopes. Quercus leaf litter moved from where it dropped into litter traps to where it settled on the forest floor, shifting the location of litter C inputs to the soil. Local variation in aboveground C rivals regional variation across regions and has the potential to introduce error when scaling C measures from points to landscapes.

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)0.003
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 score0.908
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.277
Teacher spread0.257 · 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

Citations54
Published2016
Admission routes1
Has abstractyes

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