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Record W2018324733 · doi:10.1080/00103620701548639

Dry Combustion Carbon, Walkley–Black Carbon, and Loss on Ignition for Aggregate Size Fractions on a Toposequence

2007· article· en· W2018324733 on OpenAlexfundno aff
Alie Kamara, Edward R. Rhodes, Patrick A. Sawyerr

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsSoil waterCarbon blackCarbon fibersCombustionTotal organic carbonLoss on ignitionAggregate (composite)MineralogyConversion factorSoil scienceChemistryEnvironmental scienceEnvironmental chemistryMaterials scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract Accurate and simple methods for determining soil organic carbon that do not involve the use of chemicals that damage the environment are required. Dry combustion carbon, Walkley–Black carbon, and loss on ignition (LOI) were determined on whole soils (<2000 µm) and aggregate size fractions (250–2000 µm, 53–250 µm, <53 µm) collected from a toposequence, part cropped and part under forest regrowth. For each group, the Walkley–Black correction factor averaged 1.2, which was less than the usually assumed factor of 1.3. Both temperature and sample weight had significant effects on LOI, but the former had a greater effect. There were strong correlations between LOI and dry combustion carbon for all aggregate size fractions. Highest coefficient of determination (R2=0.89–0.93) were obtained for 1‐g soil samples ignited at 375°C for 2 h; under these conditions, slopes and intercepts of the regressions of organic carbon on LOI were not significantly different among aggregate fractions.

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)0.002
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 score0.167
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.270
Teacher spread0.237 · 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

Citations23
Published2007
Admission routes1
Has abstractyes

Explore more

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