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Record W2534705478 · doi:10.1111/ele.12690

Belowground carbon flux links biogeochemical cycles and resource‐use efficiency at the global scale

2016· article· en· W2534705478 on OpenAlexfundno aff
Allison L. Gill, Adrien C. Finzi

Bibliographic record

VenueEcology Letters · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
FundersEnvironment CanadaOak Ridge Institute for Science and EducationUniversité LavalU.S. Department of EnergyNational Science Foundation
KeywordsBiogeochemical cyclePrimary productionBiomeEnvironmental scienceBiosphereBiogeochemistryBorealNutrientNutrient cycleEcosystemTropicsEcologyTaigaCarbon cycleTerrestrial ecosystemGlobal changeLatitudeCyclingProductivityBiologyClimate changeForestryGeography

Abstract

fetched live from OpenAlex

Nutrient limitation is pervasive in the terrestrial biosphere, although the relationship between global carbon (C) nitrogen (N) and phosphorus (P) cycles remains uncertain. Using meta-analysis we show that gross primary production (GPP) partitioning belowground is inversely related to soil-available N : P, increasing with latitude from tropical to boreal forests. N-use efficiency is highest in boreal forests, and P-use efficiency in tropical forests. High C partitioning belowground in boreal forests reflects a 13-fold greater C cost of N acquisition compared to the tropics. By contrast, the C cost of P acquisition varies only 2-fold among biomes. This analysis suggests a new hypothesis that the primary limitation on productivity in forested ecosystems transitions from belowground resources at high latitudes to aboveground resources at low latitudes as C-intensive root- and mycorrhizal-mediated nutrient capture is progressively replaced by rapidly cycling, enzyme-derived nutrient fluxes when temperatures approach the thermal optimum for biogeochemical transformations.

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 score0.217
Threshold uncertainty score0.274

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 head0.008
GPT teacher head0.196
Teacher spread0.187 · 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

Citations127
Published2016
Admission routes1
Has abstractyes

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