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Record W2386771400

High Yield Potential Research of Main Grain and Oil Crops in Guizhou IV the Productive Potential Research of Land

2008· article· en· W2386771400 on OpenAlexvenueno aff
Qian Xiao-gang

Bibliographic record

VenueSeed · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicRemote Sensing and Land Use
Canadian institutionsnot available
Fundersnot available
KeywordsRepresentativeness heuristicYield (engineering)AgronomyEnvironmental scienceConnotationMathematicsGrain yieldDivision (mathematics)Agricultural engineeringStatisticsEngineeringBiologyPhysics
DOInot available

Abstract

fetched live from OpenAlex

This paper studied the problems of land productive potential with Super-high-yield crops.This research chose the leading indexes which had representativeness and can directly reflect the soil function,consulted and followed the division standard of soil rank in Guizhou Province to carry on the rank division to Guizhou soil,and determined the soil function according to the size of soil restriction factors with each grade,and considerd the soil productive potential's connotation as: the production ability with the exception of the artificial intervention measures(including project,fertilizing,etc.).Calculated the rice soil productive potential of Guizhou Province by this theory,and it has taken the experiment on super-high-yield control of the middle-yield field in Huaxi district of Guiyang as the confirmation,the soil productive potential is so in line with the actual results.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.292
Teacher spread0.230 · 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 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

Citations0
Published2008
Admission routes1
Has abstractyes

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