Biochar is a long-lived form of carbon removal, making evidence-based CDR projects possible
Bibliographic record
Abstract
Abstract Science should drive policies and regulations to ensure a sustainable (environmentally, socially, and economically) green transition to a Net-Zero / Net-Negative circular economy. Since 2015, which saw COP21 in Paris, Net Zero has been a global target that must be rapidly accompanied by a Net Negative strategy to mitigate climate change. Accordingly, biochar's role as a durable carbon removal method is gaining attention and increasing. In this work, we discuss the durability of the carbon in biochar and the need for analytical techniques to support stakeholders on a project level. The different ecologically relevant groups of carbon forms contained in biochar are presented, and possible project-based methods to assess the quality and durability of the product versus the regulatory requirements for the permanence of carbon removals are summarized. Biochar is today one of the CDR technologies with the highest technology readiness level (TRL 8–9) that can ensure permanent removals for time frames relevant to climate change mitigation projects, combined with co-benefits that are gaining relevance in terms of mitigating climate impacts in agricultural soils. Graphical Abstract
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.009 |
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".