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Record W2177192348 · doi:10.4141/cjss10020

Cryosolic soils of Canada: Genesis, distribution, and classification

2011· article· en· W2177192348 on OpenAlexaffvenueabout
C. Tarnocai, James G. Bockheim

Bibliographic record

VenueCanadian Journal of Soil Science · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPermafrostSubsoilSoil waterEnvironmental scienceTotal organic carbonSoil carbonSoil scienceEarth scienceSoil horizonGeologyEnvironmental chemistryChemistry

Abstract

fetched live from OpenAlex

Tarnocai, C. and Bockheim, J. G. 2011. Cryosolic soils of Canada: Genesis, distribution, and classification. Can. J. Soil. Sci. 91: 749–762. Cryosols are permafrost-affected soils whose genesis is dominated by cryogenic processes, resulting in unique macromorphologies, micromorphologies, thermal characteristics, and physical and chemical properties. In addition, these soils are carbon sinks, storing high amounts of organic carbon collected for thousands of years. In the Canadian soil classification, the Cryosolic Order includes mineral and organic soils that have both cryogenic properties and permafrost within 1 or 2 m of the soil surface. This soil order is divided into Turbic, Static and Organic great groups on the basis of the soil materials (mineral or organic), cryogenic properties and depth to permafrost. The great groups are subdivided into subgroups on the basis of soil development and the resulting diagnostic soil horizons. Cryosols are commonly associated with the presence of ground ice in the subsoil. This causes serious problems when areas containing these soils are used for agriculture and construction projects (such as roads, town sites and airstrips). Therefore, where Cryosols have high ice content, it is especially important either to avoid these activities or to use farming and construction methods that maintain the negative thermal balance.

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 score0.042
Threshold uncertainty score0.747

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)0.0010.000

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 head0.053
GPT teacher head0.212
Teacher spread0.159 · 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

Citations69
Published2011
Admission routes3
Has abstractyes

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