Guidelines for improved quantification and reporting of carbon stocks and additional carbon storage in agroforestry systems
Bibliographic record
Abstract
Abstract The number of scientific publications related to biomass carbon or soil organic carbon under various land management practices has globally and dramatically increased during the last two decades, the same applies to the peer reviewed Agroforestry Systems journal. However, the quality of papers on carbon sequestration in agroforestry systems is very heterogeneous, and many studies do not fulfil simple requirements that would ensure the scientific value of these studies, resulting in high rates of rejections before and after review. The aim of this paper, co-authored by the Editor-in-Chief and Associate Editors of the Agroforestry Systems journal is to provide some basic guidelines to improve the quantification and reporting of carbon stocks and additional carbon storage in agroforestry systems, and to maximize manuscript acceptance. These guidelines are also of use for any other international peer-reviewed journal publishing studies on this topic. We also provide a checklist, for both authors and reviewers, of compulsory and recommended variables to be included before submission of an original study related to soil and/or biomass carbon stocks and sequestration in agroforestry systems.
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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 | 0.337 | 0.598 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".