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Effects of drip irrigation frequency on the yield and nutrient utilization efficiency of tomato under long-season cultivation in solar greenhouse.

2023· article· en· W4378516671 on OpenAlexaff
Qingqing Meng, Feng-Ling Wu, Min Wei, Lun Meng, Jing Li, Fengjuan Yang

Bibliographic record

VenuePubMed · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsMinistry of Agriculture
Fundersnot available
KeywordsDrip irrigationFertilizerNutrientDry matterGreenhouseAgronomyIrrigationWater-use efficiencyEnvironmental scienceChemistryBiology

Abstract

fetched live from OpenAlex

treatment, plant dry matter accumulation and the accumulation of N, P and K increased by 4.9%, 8.0%, 8.0%, 16.8%, the partial productivity of fertilizer and the utilization efficiency of water increased by 142.8% and 12.2%, the use efficiency of N, P and K was better than CK by 241.4%, 466.6% and 235.9%, respectively, and the tomato yield increased by 12.2%. Under the experimental conditions, drip irrigation with the Yamazaki nutrient solution at a frequency of 4 days could increase the tomato yield, as well as the use efficiency of nutrients and water. Under long-season cultivation, these trends would result in considerable saving of water and fertilizer. Overall, our findings provided a basis for improving the scientific management of water and fertilizers under long-season tomato cultivation in protected facilities.

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 score0.593
Threshold uncertainty score0.101

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.045
GPT teacher head0.223
Teacher spread0.178 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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