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Record W2085324066 · doi:10.7202/020501ar

Classement des sols selon leurs possibilités d’utilisation agricole

2005· article· en· W2085324066 on OpenAlexvenueaboutno aff
Auguste Mailloux, Armand Dubé, Lauréan Tardif

Bibliographic record

VenueCahiers de géographie du Québec · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationClass (philosophy)Value (mathematics)ForestryPastureGeographyComputer scienceMathematicsPolitical scienceStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a binary soil capability classification System which bas been developed and used in Québec since 1958. The system bas been designed to provide some basic and essential information, regarding the value of the soil, to be integrated in a broad agricultural and economic survey of the region. The basic criteria of the present system are : 1. the fundamental value of the soil determined by the characteristics of the soil profile ; and, 2. the kind and importance of management practices required, v.g. : correction of unfavourable internal or external limitations ; or, indications for a restricted use of the land such as : permanent pasture, wildlife, recreation and forestry. In this system, the capability class is derived by integrating these two variables ; or, in other words, the class is the result of the combination of these two fundamental criteria. The close relation between these two factors or parameters is ex-pressed in the scheme presented in tables I and II for mineral and organic soils respectively. This logical and practical method seems suitable and adaptable to describe any set of landscapes which are of interest to agronomists, economists and regional planning commissions. The regions of Bas-Saint-Laurent, Gaspésie, Iles-de-la-Madeleine and Montréal have been mapped according to this system.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.020
GPT teacher head0.226
Teacher spread0.206 · 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 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

Citations2
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueCahiers de géographie du QuébecSame topicAgriculture and Rural Development ResearchFrench-language works237,207