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Record W2282024250 · doi:10.14288/1.0095436

A method for developing soil management units

2010· article· en· W2282024250 on OpenAlexaboutno aff
Zweck von Zweckenburg

Bibliographic record

VenueOpen Collections · 2010
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceComputer scienceBusiness

Abstract

fetched live from OpenAlex

The purpose of the present study was to develop a quantitative methodology to define optimum land use systems. Soil survey data were assessed for capability, suitability and feasibility uses to establish interpretative soil units for which planning and management recommendations could be made. The study was agriculturally oriented due to the availability of agricultural productivity data for suitability assessments from four sources: farmer survey, direct estimate by expert consensus, research station data and plot trials-Soils were grouped using cluster analysis on the basis of permanent inherent soil properties. The technique did not group the soils satisfactorily for management purposes due to statistical limitations of the procedure in assessing overlapping and interdependent variables, such as soil characteristics, and restrictions imposed by the soils data set which was neither adequately large nor diverse to form multimember soil groups. Stepwise discriminant analysis was more successful in assessing the interpretative soils data and in identifying discriminant soil parameters. The Canada Land Inventory derived capability classes were separated by drainage, the quantitatively defined suitability classes were separated by parent material and the socioeconomically defined feasibility groups were separated by pH and coarse fraction. Comparison of the interpretative soils classifications revealed that the capability ratings overestimated actual measured yield and that current land use did not realize the full agricultural potential of the land. Feasibility, unlike capability and suitability, stressed parameters other than soil properties in land evaluation. The suitability assessment based upon actual observed productivity data measured under real market conditions was recommended as the most quantitative land evaluation approach. Other soils can be added to the open ended system and optimal use can be made of all soils using guidelines developed by key farmers under real market conditions for soils suitability groups.

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)0.005
metaresearch head score (Gemma)0.015
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: Methods · Consensus signal: Methods
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.005

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 head0.022
GPT teacher head0.280
Teacher spread0.257 · 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
GenreMethods

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
Published2010
Admission routes1
Has abstractyes

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