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Record W2562568621 · doi:10.1038/srep39307

Yields and Nutritional of Greenhouse Tomato in Response to Different Soil Aeration Volume at two depths of Subsurface drip irrigation

2016· article· en· W2562568621 on OpenAlexaff
Yuan Li, Wenquan Niu, Miles Dyck, Jingwei Wang, Xiaoyang Zou

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsUniversity of Alberta
FundersHigher Education Discipline Innovation Project
KeywordsAerationSugarIrrigationGreenhouseDrip irrigationAgronomyEnvironmental scienceYield (engineering)Volume (thermodynamics)ChemistryHorticultureAnimal scienceBiologyFood scienceMaterials science

Abstract

fetched live from OpenAlex

This study investigated the effects of 4 aeration levels (varied by injection of air to the soil through subsurface irrigation lines) at two subsurface irrigation line depths (15 and 40 cm) on plant growth, yield and nutritional quality of greenhouse tomato. In all experiments, fruit number, width and length, yield, vitamin C, lycopene and sugar/acid ratio of tomato markedly increased in response to the aeration treatments. Vitamin C, lycopene, and sugar/acid ratio increased by 41%, 2%, and 43%, respectively, in the 1.5 times standard aeration volume compared with the no-aeration treatment. An interaction between aeration level and depth of irrigation line was also observed with yield, fruit number, fruit length, vitamin C and sugar/acid ratio of greenhouse tomato increasing at each aeration level when irrigation lines were placed at 40 cm depth. However, when the irrigation lines were 15 cm deep, the trend of total fruit yields, fruit width, fruit length and sugar/acid ratio first increased and then decreased with increasing aeration level. Total soluble solids and titrable acid decreased with increasing aeration level both at 15 and 40 cm irrigation line placement. When all of the quality factors, yields and economic benefit are considered together, the combination of 40 cm line depth and "standard" aeration level was the optimum combination.

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)0.001
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 score0.469
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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

Citations67
Published2016
Admission routes1
Has abstractyes

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