Cover crops do increase soil organic carbon stocks—A critical comment on Chaplot and Smith (2023)
Bibliographic record
Abstract
Most of the exclusion criteria are not justified. We conclude that the doubts on the positive effects of cover crops on SOC are unjustified. Moreover, their opinion on the relevance of policies and the use of public subsidies is a political point of view that is expressed without any nuance and is very debatable given the obvious weaknesses of their study. Finally, it is alarming that this manuscript even passed the review process of such a high impact journal. Christopher Poeplau: Conceptualization; writing – original draft. Zhi Liang: Conceptualization; writing – review and editing. Axel Don: Conceptualization; writing – review and editing. Daria Seitz: Writing – review and editing. Chiara De Notaris: Writing – review and editing. Denis Angers: Writing – review and editing. Pierre Barré: Writing – review and editing. Damien Beillouin: Writing – review and editing. Rémi Cardinael: Writing – review and editing. Eric Ceschia: Writing – review and editing. Claire Chenu: Writing – review and editing. Julie Constantin: Writing – review and editing. Julien Demenois: Writing – review and editing. Bruno Mary: Writing – review and editing. Sylvain Pellerin: Writing – review and editing. Daniel Plaza-Bonilla: Writing – review and editing. Miguel Quemada: Writing – review and editing. Eric Justes: Conceptualization; writing – original draft. We do not have a conflict of interest to declare. There is no data involved in this letter.
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 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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.051 | 0.042 |
| Insufficient payload (model declined to judge) | 0.011 | 0.012 |
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".