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Record W2098670526 · doi:10.4141/s05-061

Particle densities of wetland soils in northern Alberta, Canada

2006· article· en· W2098670526 on OpenAlexfundvenueaboutno aff
Todd Redding, K. J. Devito

Bibliographic record

VenueCanadian Journal of Soil Science · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLoss on ignitionPeatSoil waterWetlandOrganic matterEnvironmental scienceBulk densitySedimentParticle (ecology)Soil scienceSoil organic matterRange (aeronautics)Particle densitySoil textureHydrology (agriculture)Environmental chemistryChemistryGeologyEcologyGeomorphologyVolume (thermodynamics)OceanographyPhysicsMaterials science

Abstract

fetched live from OpenAlex

Particle density is a fundamental soil physical property, yet values of soil and organic matter particle density (ρ s and ρ o ) vary widely in the literature. We measured particle density of organic soils from five wetland types, and from exposed sediments of drying ponds, in northern Alberta, Canada. Our measured values of organic soil and pond sediment ρ s varied widely (1.43–2.39 Mg m -3 ); however, calculated values of ρ o (1.34–1.52 Mg m -3 ) were relatively constant. The measured and calculated ρ s and ρ o values were similar to those obtained in published studies using similar methods, but were higher than the values provided in many reference texts. Given the relatively small variability in ρ o , the use of mean values of ρ o , combined with measurements of organic matter loss-on-ignition, shows promise as a simple method for obtaining reliable estimates of ρ s across a range of wetland types. Key words: Particle density, peat, organic matter, wetland soil, loss-on-ignition

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 score0.007
Threshold uncertainty score0.357

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 studies0.0000.001
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 head0.005
GPT teacher head0.174
Teacher spread0.169 · 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

Citations65
Published2006
Admission routes3
Has abstractyes

Explore more

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