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Record W4320516449 · doi:10.5539/jas.v15n3p85

Response of Ozone Treatment on Disease Incidence, Dissolved Oxygen Levels, Growth and Yield of Cucumber Crop Grown in Hydroponics in Cooled Green House. Season: Summer (June-August) at DGALR, Rumais

2023· article· en· W4320516449 on OpenAlexvenueno aff
Muthir S. Al-Rawahy, Waleed S. Al-Abri, Alya S. Al-Hinai, Hussain A. Al-Abri, Siham H. Al-Mahrooqi, Narjis M. Al-Shmali, Zainab T. Al-Khatri, Khalifa S. Al-Subhi

Bibliographic record

VenueJournal of Agricultural Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInnovations in Aquaponics and Hydroponics Systems
Canadian institutionsnot available
Fundersnot available
KeywordsHydroponicsOzoneYield (engineering)CropNutrientPythiumHorticultureAgronomyBiologyChlorophyllAnimal scienceChemistryEcology

Abstract

fetched live from OpenAlex

One of the main concerns related to closed systems is the potential spread of root pathogens. With the recirculation of nutrient solutions. Ozone treatment was tested for the efficacy against plant pathogen (Pythium), growth and yield of cucumber crop grown in hydroponic closed system during summer season (June-August) 2022. Two nutrient solution feeding tanks were used one with ozone treatment and other without ozone treatment in randomized complete design (RCD) with four replication. The results showed significant (p < 0.05) differences were observed between the treatments in diseases infections with pythium diseases. No significant (p < 0.05) differences were detected in chlorophyll content, as SPAD values and yield between the two treatment of cucumber statistically. Ozone treated plants produced more yield 4.0 ton/gh.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

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)0.0020.000

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 head0.035
GPT teacher head0.263
Teacher spread0.228 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicInnovations in Aquaponics and Hydroponics SystemsFrench-language works237,207