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Record W2026503066 · doi:10.13031/2013.9404

Controlled Aeration During Rice Storage: Effects of Geographic Location on Insect Survival

2002· article· en· W2026503066 on OpenAlexfundno aff
Terry A. Howell, J. F. Murdoch, F. H. Arthur, D. R. Gardisser

Bibliographic record

Venue2002 Chicago, IL July 28-31, 2002 · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pest Control Strategies
Canadian institutionsnot available
FundersArkansas Rice Research and Promotion BoardMcMaster University
KeywordsAerationEnvironmental scienceBinBiologyHorticultureAgronomyEcologyMathematics

Abstract

fetched live from OpenAlex

Alternative storage strategies in grains are critical in combating insect problems, especially withmany traditional chemicals being threatened for reduction. This work examined the use of controlled ambientaeration to reduce the temperature and to inhibit insect populations in stored rice at three geographicallydifferent locations in Arkansas. Cypress rice at a northeast (NE) and central (CEN) storage location and cv.cocodrie at a southeast (SE) location were stored for one season. Half of the bins at each site were aeratedtraditionally, and the remaining bins were aerated with a thermostatically-activated controller to reduce thetemperatures within the bins. Insects, in cages, were placed in each bin, and the cages were sampledperiodically to determine their viability. The ambient conditions available for aeration control were notsignificantly different from one another. Temperatures within the bins aerated by the controller were notsignificantly lower than those in manually-aerated bins. Live insects recovered at each sampling time werereduced with storage duration, and fewer were recovered from the SE location (most likely due to cultivar).Total recovered insects, after the rice from each cage was allowed to incubate, were reduced with the aerationcontroller in addition to the previously mentioned parameters.

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 score0.952
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.191
Teacher spread0.178 · 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
Published2002
Admission routes1
Has abstractyes

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Same venue2002 Chicago, IL July 28-31, 2002Same topicInsect Pest Control StrategiesFrench-language works237,207