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Record W4220837258 · doi:10.1007/s11104-022-05390-9

Sinks for plant surplus carbon explain several ecological phenomena

2022· article· en· W4220837258 on OpenAlexaff
Cindy E. Prescott

Bibliographic record

VenuePlant and Soil · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhotosynthesisRhizosphereBiologyUnderstoryBotanyNutrientMyrmecophytePlant physiologyCanopyCarbon fixationEcologyNectar

Abstract

fetched live from OpenAlex

Abstract Plants engage in many processes and relationships that appear to be wasteful of the high-energy compounds that they produce through carbon fixation and photosynthesis. For example, living trees keep leafless tree stumps alive ( i.e. respiring) and support shaded understory trees by sharing carbohydrates through root grafts or mycorrhizal fungal networks. Plants exude a diverse array of organic compounds from their roots and leaves, which support abundant rhizosphere and phyllosphere microbiomes. Some plants release substantial amounts of sugar via extra-floral nectaries, which enrich throughfall and alter lichen communities beneath the canopy. Large amounts of photosynthetically fixed carbon are transferred to root associates such as mycorrhizal fungi and N-fixing micro-organisms. Plants also respire fixed C through an alternative pathway that does not generate ATP. Rates of each of these processes appear to be highest when plants are growing under mild-to-moderate deficiencies of nutrients or water. During this stage of deficiency, aboveground plant growth is curtailed more than photosynthesis, causing leaves to produce surplus carbohydrates. Each of the above phenomena provide a sink for these surplus carbohydrates, thereby preventing feedback inhibition of photosynthesis, and perpetuating the influx of C. Because these processes incur little cost to the source plant, they need not provide a benefit beyond the removal of surplus carbohydrates.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.342

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 head0.024
GPT teacher head0.190
Teacher spread0.165 · 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 designNot applicable
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

Citations42
Published2022
Admission routes1
Has abstractyes

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