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Record W2925355681 · doi:10.3390/soilsystems3020026

Variation in Feedstock Wood Chemistry Strongly Influences Biochar Liming Potential

2019· article· en· W2925355681 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSoil Systems · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Toronto
FundersFord Motor Company
KeywordsBiocharPyrolysisChemistryRaw materialAmendmentSoil waterEnvironmental chemistryOrganic chemistrySoil scienceEnvironmental science

Abstract

fetched live from OpenAlex

Chars intended for use as soil amendment (“biochars”) vary greatly in their chemical and physical properties. In the present study, 19 Canadian temperate wood feedstocks were charred across a range of pyrolysis temperatures from 300–700 °C. The resulting 95 biochars were tested for their physio-chemical properties and liming capacity. Data indicated increasing base cation concentrations including Ca, Mg, and K (elements that characteristically form liming compounds, i.e., carbonates) as pyrolysis temperature increased. Acidic surface functional groups were analyzed with modified Boehm titration: Carboxylic and lactonic functional group concentrations decreased and phenolic group concentration increased with pyrolysis temperature. Functional group composition also varied greatly with feedstock: In particular, conifer-derived biochars produced at pyrolysis temperatures <500 °C showed much higher carboxylic and lactonic functional group concentrations than did angiosperm-derived biochars. Liming capacity was assessed using soil incubation experiments and was positively related to biochar pH. Both acidic surface functional group concentration and nutrient element concentration influenced biochar pH: we developed a non-linear functional relationship that predicts biochar pH from the ratio of carboxylic to phenolic moieties, and concentrations of Ca and K. Biochar’s liming components that are inherited from feedstock and predictably modified by pyrolysis temperature provide a basis for optimizing the production of biochar with desired pH and liming characteristics.

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.

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 score0.920
Threshold uncertainty score0.459

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 head0.008
GPT teacher head0.189
Teacher spread0.181 · 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