Research and Application of Biochar in North America
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.
Bibliographic record
Abstract
Biochar production and its application in soils have been proposed as a good strategy for carbon sequestration, providing simultaneous benefits for improving soil quality and increasing agronomic productivity. In this chapter, we summarize historical and current research and application of biochar in North America, focusing on three important aspects of biochar as (i) a soil amendment, (ii) a carbon sequestration agent, and (iii) a high-value carbon material. The effect of biochar as a soil amendment on agronomic yields was comprehensively evaluated. Application of biochar to fertile soil was examined for potential synergistic agronomic effects when coapplied with nutrient fertilizers. The potential of biochar as a carbon sequestration strategy was assessed in North America by theoretically analyzing the available and unused biomass that could be used to produce biochar for carbon storage. It indicates that Canada and the United States have sufficient biomass to produce biochar and thereby offset their annual CO2 emissions from human activity to some degree. Increasing carbon retention during biochar production and improving its stability may aid adoption of biochar as a carbon sequestration agent. In addition to directly sequestering carbon, biochar as a soil amendment has potential to reduce greenhouse gas emissions from soils. Additionally, biochar can be used as a sorbent to remove contaminants or serve as a precursor of other high-value carbon materials. To increase the potential of widespread adoption of biochar, its potential risks and barriers are further addressed and analyzed, and thereby possible solutions and future application in North America are proposed.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it