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Record W2320630492 · doi:10.5986/jrafi.46.1

Detection of Irradiated Pulses by PSL Method

2011· article· en· W2320630492 on OpenAlexaboutno aff
Masayuki Sekiguchi, Seiko Nakagawa, Shunji Yunoki, Toshimi Ohyabu, Shoji Hagiwara, Setsuko Todoriki, Mikirou Tada, Katsunori Honda

Bibliographic record

VenueFOOD IRRADIATION JAPAN · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRadiation Effects and Dosimetry
Canadian institutionsnot available
Fundersnot available
KeywordsPSLIrradiationPhotostimulated luminescenceHorticultureChemistryRadiochemistryLuminescenceMaterials scienceBiologyPhysicsMathematicsOptoelectronics

Abstract

fetched live from OpenAlex

Photostimulated luminescence (PSL) as a screening method is very simple and rapid to detect irradiated foods but various disadvantages (light induced fading of PSL signal or response to clean foods with minerals insensitive to PSL measurement). In this study the characteristics of radiation induced PSL for 10 kinds of pulses (Chinese Soybean and Adzuki bean, Pinto bean, Cowpea, Green gram, Canadian Blue pea and Soybean, American Black-eyed pea and Chickpea, Red Kidney Bean) were investigated. The screening-PSL (s-PSL) cumulate counts of pulses significantly increased with irradiation dose up to 3kGy. The s-PSL cumulate counts of irradiated pulses gradually decreased with increasing storage periods. The s-PSL cumulate counts of all pulse samples irradiated at a minimum dose of 0.5kGy exceeded considerably the upper screening threshold (5000 counts) regardless of storage period. Calibrated PSL (Cal-PSL) were obtained by re-irradiating the pulse samples with a gamma ray dose of 1kGy and the PSL ratios (s-PSL/Cal-PSL) were calculated for normalization of sensitivity of the pulse samples. The PSL ratio at each irradiation dose was almost similar regardless of kind of pulses.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.694

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)0.0010.000

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 head0.021
GPT teacher head0.215
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 designObservational
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
Published2011
Admission routes1
Has abstractyes

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Same venueFOOD IRRADIATION JAPANSame topicRadiation Effects and DosimetryFrench-language works237,207