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Record W2286159336 · doi:10.13031/2013.4053

Mechanical Damage to Soybean Seed during Processing

2001· article· en· W2286159336 on OpenAlexaff
Shreekant R. Parde, Rameshwar T. Kausal, Digvir S. Jayas, N. D. G. White

Bibliographic record

Venue2001 Sacramento, CA July 29-August 1,2001 · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Manitoba
Fundersnot available
KeywordsGerminationWater contentAccelerated agingMoistureHorticultureElevatorSpecific gravityEnvironmental scienceAgronomyMaterials scienceEngineeringBiologyComposite materialGeotechnical engineering

Abstract

fetched live from OpenAlex

The effects of seed cleaning and handling on soybean seed germination and physicalintegrity were determined with changing seed moisture content. In addition, storage behavior ofseed and loss of storability caused by damage resulting from free-fall from different heightswere determined.Six lots of the variety MACS-13 at three different moisture contents were passedthrough a vertical bucket elevator, cleaner with grader, and gravity separator and evaluated formechanical damage, germination, and vigor index. The storage behavior of the lots, at differentstages of processing, was studied by performing an accelerated aging test. The effect of free-fallon quality of the seed was studied by dropping six seed lots from four different heights on tocement and galvanized iron floors. The vertical bucket elevator significantly decreased germination and increased splits andseed coat damage. The seed lots at 12% moisture content (m.c.) (dry basis; all moisturecontents are reported on a dry basis), suffered less loss in seed quality than the lots at 10 or11% m.c. The storage quality of seed, as predicted by the accelerated aging test, at 12% m.c.was also better than the lots at 10 or 11% m.c. A free-fall of soybean seed from differentheights on to the cement floor resulted in greater loss in quality than when dropped on to thegalvanized iron floor.

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 categoriesInsufficient payload (model declined to judge)
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 score0.876
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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.

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
Published2001
Admission routes1
Has abstractyes

Explore more

Same venue2001 Sacramento, CA July 29-August 1,2001Same topicSoybean genetics and cultivationFrench-language works237,207