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Record W6903290773 · doi:10.1139/cjss-2014-074

Equivalent latitude for prediction of soil development in a complex mapunit

2015· article· en· W6903290773 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Geostatistics and Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsLatitudeSoil waterContext (archaeology)EcoregionSoil classificationRange (aeronautics)

Abstract

fetched live from OpenAlex

Whitson, I. R. 2015. Equivalent latitude for prediction of soil development in a complex mapunit. Can. J. Soil Sci. 95: 125-137. Soil pattern in the Hillwash complex mapunit from Saskatchewan is too variable to be resolved spatially with conventional mapping approaches. The equivalent latitude metric allows identification of an index based on gradient and aspect that ranks sites based on differences in direct radiant energy inputs. Effects on soil development with reference to surface horizon color and soil classification were investigated at three study areas in southern Saskatchewan. At the first, sites with equivalent latitude greater than local latitude (north group) had a higher frequency of darker soil colors than sites where equivalent latitude was less than local latitude (south group). Black Chernozemic profiles made up nine of 13 profiles from the north group compared with none in the south group or in local controls. Similar color and classification trends in a north sample group were found at a second study area. Results from a third study area more than 200 km away and in a drier ecoregion found similar differences albeit a different set of subgroups between north and south group soils at that location. The equivalent latitude metric could be used in a GIS context to better resolve soil characteristics within this complex mapunit, but only after additional work to include a climate parameter such as potential transpiration into the model.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.705

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 head0.485
GPT teacher head0.271
Teacher spread0.214 · 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
Published2015
Admission routes1
Has abstractyes

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