Variation in Feedstock Wood Chemistry Strongly Influences Biochar Liming Potential
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".