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Record W3084951516 · doi:10.1016/j.tree.2020.08.007

Surplus Carbon Drives Allocation and Plant–Soil Interactions

2020· review· en· W3084951516 on OpenAlexaff
Cindy E. Prescott, S. J. Grayston, Heljä‐Sisko Helmisaari, Eva Kaštovská, Christian Körner, Hans Lambers, Ina C. Meier, Peter Millard, Ivika Ostonen

Bibliographic record

VenueTrends in Ecology & Evolution · 2020
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhotosynthesisNutrientCarbon fixationCarbon fibersEcosystemBiologyAgronomyBotanyEcology

Abstract

fetched live from OpenAlex

Plant growth is usually constrained by the availability of nutrients, water, or temperature, rather than photosynthetic carbon (C) fixation. Under these conditions leaf growth is curtailed more than C fixation, and the surplus photosynthates are exported from the leaf. In plants limited by nitrogen (N) or phosphorus (P), photosynthates are converted into sugars and secondary metabolites. Some surplus C is translocated to roots and released as root exudates or transferred to root-associated microorganisms. Surplus C is also produced under low moisture availability, low temperature, and high atmospheric CO 2 concentrations, with similar below-ground effects. Many interactions among above- and below-ground ecosystem components can be parsimoniously explained by the production, distribution, and release of surplus C under conditions that limit plant growth.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.281
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations367
Published2020
Admission routes1
Has abstractno

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