MétaCan
Menu
Back to cohort
Record W4377103230 · doi:10.3390/agronomy13051394

Optimizing Biochar Particle Size for Plant Growth and Mitigation of Soil Salinization

2023· article· en· W4377103230 on OpenAlexaff
Esther P.Y. Tang, Wenxi Liao, Sean C. Thomas

Bibliographic record

VenueAgronomy · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiocharParticle sizeBiomass (ecology)Soil waterEnvironmental scienceAgronomyParticle (ecology)SalinityChemistrySoil sciencePyrolysisBiologyEcology

Abstract

fetched live from OpenAlex

Pyrolyzed waste biomass, or biochar, has been suggested as a means to increase plant growth and mitigate soil salinization, which is a widespread agricultural issue and can reach extreme levels in urban soils impacted by de-icing salts. Soil mixing is enhanced by reduced biochar particle size; however, biochar properties vary with particle size, and recent studies have suggested that plant growth responses may be maximized at intermediate particle sizes. We examined the responses of two plant species (cowpea (Vigna unguiculata) and velvetleaf (Abutilon theophrasti)) to biochar amendments that spanned a wide range of particle sizes obtained by sieving, with and without de-icing salt additions. The smallest size fractions of biochar reduced plant growth relative to unamended controls. Plant biomass production was generally maximized at intermediate biochar particle size treatments, with particle sizes of 0.5–2.0 mm showing the best response. Mitigation of salt effects was also improved at intermediate biochar particle sizes in this particle size range. Our results emphasize the importance of optimizing biochar particle size to best enhance plant responses to biochar, with particular reference to saline soils.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.078

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 head0.016
GPT teacher head0.210
Teacher spread0.194 · 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 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

Citations22
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAgronomySame topicSoil Carbon and Nitrogen DynamicsFrench-language works237,207