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Record W4380683408 · doi:10.9734/ijpss/2023/v35i153104

Physical Attributes of Bael (Aegle marmelos L.) Fruit and Suitability for Commercialization and Processing

2023· article· en· W4380683408 on OpenAlexaff
Suman Kumar, R. B. Ram, Awadhesh Kumar, Raveender Kumar, Rajeev Kumar, Malay Marut Sharma

Bibliographic record

VenueInternational Journal of Plant & Soil Science · 2023
Typearticle
Languageen
FieldNursing
TopicFood Science and Nutritional Studies
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsPulp (tooth)Randomized block designOrchardHorticultureCommercializationMathematicsBiologyMedicineDentistry

Abstract

fetched live from OpenAlex

The present experiment entitled “Studies on physical attributes of bael (Aegle marmelos L.) fruit suitable for commercialization and processing” was conducted at Horticulture laboratory of the Department of Applied Plant Science during 2011-12. Matured fruits procured from well established bael orchard and investigation was carried out Completely Randomized Block Design with three replications. The observations were recorded on fruit shape, skull colour, pulp colour, fruit weight, skull thickness, fruit length, fruit width, pulp content and seed content. Flattened round fruit shape, greenish skull colour and pale yellow in pulp colour was found in NB-7. Minimum skull thickness 3.10 mm, seed content 2.42 per cent, whereas maximum fruit weight 2.00 kg, fruit length 16.20 cm, fruit width 17.20 cm and pulp content 79.20 per cent was found in cv. Narendra Bael-7. Result indicated that on the basis of physical attributes NB-7 varieties was found best for commercialization and processing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.042
GPT teacher head0.338
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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