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Record W2477178972 · doi:10.1061/9780784413678.ch13

Carbon Immobilization by Enhanced Photosynthesis of Plants

2015· book-chapter· en· W2477178972 on OpenAlexfundno aff
Nouha Klai, Archana Kumari, Yan Song, R. D. Tyagi, Tian C. Zhang

Bibliographic record

VenueAmerican Society of Civil Engineers eBooks · 2015
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsnot available
FundersCanada Research ChairsWorld Resources InstituteCalifornia Department of Transportation
KeywordsPhotosynthesisCarbon fibersEnvironmental scienceMaterials scienceBotanyBiologyComposite material

Abstract

fetched live from OpenAlex

This chapter reviews the implementation of four major strategies to mitigate carbon emissions through forestry activities, briefly describing the concepts and activities related to deforestation, reforestation, and afforestation. The chapter then talks about genetic engineering to increase C4 plants for carbon dioxide fixation. Economic options use market forces to encourage activities reducing deforestation and/or forestry activities. The chapter explains the evolution of manipulation of ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) in plants. It discusses manipulation methods of RuBisCO in plants and explains the increase of C4 carbon fixation photosynthetic plants. The chapter also discusses future trends and possible challenges associated with the strategies. C4 photosynthesis is important for understanding the origin and function of the modern biosphere. Recent advances in genomics and new evolutionary and developmental studies prove the discovery of the key genes controlling the expression of C4 photosynthesis.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.005

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 head0.014
GPT teacher head0.197
Teacher spread0.183 · 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 designBench or experimental
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

Citations0
Published2015
Admission routes1
Has abstractyes

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