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Record W4393243637 · doi:10.53555/sfs.v8i3.2384

Enhancing Crop Productivity Through Soilless Cultivation: A Comprehensive Review

2022· review· en· W4393243637 on OpenAlexvenueno aff
Nitisha Dabral, Rajat Pratap Singh, Mahipal Singh

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityCropAgricultural engineeringCrop productivityAgronomyCrop productionCrop managementAgroforestryAgricultural economicsEnvironmental scienceAgricultural scienceBusinessEngineeringBiologyAgricultureEconomicsEcologyEconomic growth

Abstract

fetched live from OpenAlex

Hydroponics, or soilless growing, is an innovation in agriculture that provides a lasting solution to the world's food security problems. With soilless cultivation, carefully manage the environmental conditions of your plants by growing them in nutrient-rich solutions without the need for regular soil mediums. Aeroponics stops plant roots in a misted atmosphere to maximize nutrient absorption, while hydroponics, the most extensively used approach, uses water-based nutrient solutions to support plant growth. By combining hydroponic gardening and aquaculture, aquaponics promotes symbiotic plant-fish collaborations that improve resource efficiency and cycling of nutrients. Compared to traditional soil-based agriculture, soilless cultivation has the advantage of higher agricultural yields, less water usage, and less environmental effect. In addition, soilless technologies facilitate urban agriculture and increase food resilience by allowing year-round cultivation regardless of geographic limitations.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.436
GPT teacher head0.352
Teacher spread0.084 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Survey in Fisheries SciencesSame topicCrop Yield and Soil FertilityFrench-language works237,207