MétaCan
Menu
Back to cohort

<sup>1</sup>H‐NMR Method Enables Early Identification of Degeneration in the Quality of Sweet Potato Tubers

2004· article· en· W2099589673 on OpenAlexaff
Mari Iwaya‐Inoue, Nahid Sultana, K. Saitou, Kimitoshi Sakaguchi, Masahiro Fukuyama

Bibliographic record

VenueJournal of Agronomy and Crop Science · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsNutrasource
Fundersnot available
KeywordsHorticultureChemistryArrhenius equationInverse temperatureRelaxation (psychology)Nuclear magnetic resonanceActivation energyBiologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract In sweet potato tuber, which is a tropical plant, long‐term storage leads to loss of water and carbohydrate, thus water mobility was investigated using 1 H‐NMR spectroscopy. Electrolyte leakage indicated that tubers stored at 15 °C for 1 year were partly injured and that frozen‐thawed tissues were dead. Nuclear magnetic resonance (NMR) spin–lattice relaxation time ( T 1 ) and spin–spin relaxation time ( T 2 ) clearly increased with the duration of storage, whereas these values decreased in the dead tissues. Furthermore, Arrhenius plots for T 1 and T 2 were determined at temperatures ranging from 20 to 0 °C in 2.5 °C steps. In the fresh tubers, a strong converse temperature dependency was shown in the T 2 measurement. On the contrary, there was no temperature dependency in the T 2 of the dead tissues. Thus, the existence of inverse temperature dependency reflected tissue viability. Additionally, any change in the T 2 of the fresh tubers occurred at about 14 °C, which virtually coincided with the storage temperature of 15 °C. The slope change in T 2 might have responded to a physiological change as a primary event. In conclusion, monitoring water status by NMR could provide early identification of changes in the quality of post‐harvest crops; this method shows great promise for use in environmental‐stressed crop yield research.

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)0.002
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 score0.350
Threshold uncertainty score0.167

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.017
GPT teacher head0.355
Teacher spread0.338 · 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

Citations15
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agronomy and Crop ScienceSame topicNMR spectroscopy and applicationsFrench-language works237,207