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Record W6884617734 · doi:10.1139/cjss2010-018

Effects of agriculture on the classification of Black soils in the Midwestern United States

2012· article· en· W6884617734 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Geostatistics and Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsUSDA soil taxonomySoil surveySoil waterDigital soil mappingAgricultureSoil seriesSoil classificationUnified Soil Classification System

Abstract

fetched live from OpenAlex

Veenstra, J. J. and Burras, C. L. 2012. Effects of agriculture on the classification of Black soils in the Midwestern United States. Can. J. Soil Sci. 92: 403-411. Soil surveys are generally treated as static documents. Many soil survey users assume that pedon data generated 30 to 50 yr ago still represents today's soil, as short-term changes in soil properties are perceived to be limited to the soil surface and thus pedologically insignificant. In this study, we re-sampled and re-analyzed 82 pedons with historical descriptions and laboratory data in Iowa, United States, to evaluate changes in soil profile properties and taxonomic classification after approximately 50 yr of agricultural land use. Using historical and current data, we classified sampled pedons using Canadian Soil Taxonomy, US Soil Taxonomy and the Food and Agriculture Association World Reference Base (FAO-WRB). Our results show that soil characteristics have changed significantly enough to change the classification. In each taxonomic system, the classification of 60% or more of the sampled pedons differed from the original. Classification of 15 to 32% of the sampled pedons changed at the Order (or equivalent) level with 11 to 33% of the pedons originally classified as Black soils - Mollisols, Chernozems or Phaeozems - no longer classified as Black soils. The change in soil classification over such a short-time period challenges the validity and usefulness of treating existing soil maps as static documents as well as traditional soil classification hierarchies.

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 score0.873
Threshold uncertainty score0.283

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 head0.185
GPT teacher head0.237
Teacher spread0.051 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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