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Record W2267289091 · doi:10.21273/hortsci.41.4.979b

Physical Properties of Whole Fresh-ground Parboiled Rice Hulls for Use as a Horticultural Root Substrate

2006· article· en· W2267289091 on OpenAlexaboutno aff
Johann S. Buck, Michael R. Evans, Paolo Sambo

Bibliographic record

VenueHortScience · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsParticle sizeParticle (ecology)Bulk densityParticle densityRice hullsWettingChemistryHorticultureMaterials scienceEnvironmental scienceSoil sciencePhysicsSoil waterComposite materialVolume (thermodynamics)Biology

Abstract

fetched live from OpenAlex

Horticultural root substrates are designed to provide the optimal physical properties for plant growth. These properties include bulk density (g·cm -3 ), air-filled pore space (% v/v), total pore space (% v/v), water-filled pore space (% v/v), water-holding capacity (% v/v and w/w), and wettability. Whole, fresh parboiled rice hulls were ground to produce four grades with varying particle size distributions. Particle sizes for the four grades ranged from <0.25 to >2.80 mm. Additionally, discrete particle sizes of <0.25, 0.50, 1.00, 2.00, 2.80, and >2.80 mm were produced. For all grade distributions and particle point sizes, physical properties were determined and contrasted against Canadian sphagnum peat. As the proportion of smaller particle sizes in the distributions increased or as the particle point sizes decreased, total pore space (% v/v) and air-filled pore space (% v/v) decreased, while, bulk density (g·cm -3 ) and water-holding capacity (% v/v and w/w) increased. Additionally, as the proportion of particle sizes from <0.25–0.50 mm increased, the wettabilty of the whole fresh parboiled rice hull material decreased. Particle sizes ranging from 1.00–2.80 mm possessed the physical properties most suitable for plant growth in containerized greenhouse crop production and were most similar to peat.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.414

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 head0.037
GPT teacher head0.243
Teacher spread0.206 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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