MétaCan
Menu
Back to cohort
Record W4413256808 · doi:10.5376/tgmb.2025.15.0005

High-Density Tea Planting: A Case Study in Commercial Tea Gardens

2025· article· en· W4413256808 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTree Genetics and Molecular Breeding · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsSowingGreen teaTea gardenCamellia sinensisHorticultureBiologyFood science

Abstract

fetched live from OpenAlex

This study explores the impact of high-density planting on the yield, quality and earnings of tea. High-density planting means planting more tea trees than usual in plots of the same size. In actual planting, it has been found that doing so enables better utilization of sunlight, water and nutrients in the soil, thereby increasing the yield of tea. This study analyzed the influences of factors such as planting density, tea tree varieties, and local environmental conditions on the growth of tea trees and tea yield. It is also pointed out that daily management tasks such as watering, pruning and fertilizing are very important and play a key role in managing high-density tea gardens well. Although high-density planting can increase the yield and quality of tea, it also brings some problems, such as greater difficulty in pest and disease control and easier soil degradation. This study aims to strike a balance between increasing production and protecting the environment, ensuring the long-term sustainable development of high-density tea cultivation.

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.037
GPT teacher head0.236
Teacher spread0.198 · 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