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Record W7077058113 · doi:10.5376/msb.2025.16.0006

Study on the Effects of Different Irrigation Strategies on the Yield and Quality of <i>Chrysanthemum morifolium</i>

2025· article· en· W7077058113 on OpenAlexvenueno aff

Bibliographic record

VenueMolecular Soil Biology · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsIrrigationSowingWater-use efficiencyYield (engineering)Surface irrigationDeficit irrigationDrip irrigationWater conservation

Abstract

fetched live from OpenAlex

We studied the effects of different irrigation methods on the yield, quality, stress resistance and water use efficiency of Chrysanthemum morifolium . The results showed that water-saving irrigation and precision irrigation during the growth period could significantly improve the water use efficiency without affecting the yield. If sufficient irrigation is adopted, although the yield per mu is the highest, the water use efficiency is poor. We also noticed that the amount of effective components such as flavonoids and volatile oil would change with different irrigation methods. Among them, precision irrigation can make these medicinal ingredients accumulate more. Water saving irrigation also makes chrysanthemum more drought resistant and disease resistant, and powdery mildew and leaf spot disease occur less. The study also shows that efficient irrigation methods such as drip irrigation can further improve water use efficiency and provide a good help for planting in different regions. This study provides a theoretical basis and technical methods for the efficient planting of Chrysanthemum morifolium . By adjusting the irrigation strategy, it can not only save water resources, but also promote the development of chrysanthemum industry, and also help to promote the development direction of green agriculture.

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)0.001
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 score0.366
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.020
GPT teacher head0.262
Teacher spread0.242 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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