Motor-manual release changes carbon distribution in soil and tree biomass pools in the short term
Bibliographic record
Abstract
Despite offering multiple ecosystem services, such as their ability to sequester carbon (C), regenerating mixedwood boreal forests are often only managed to increase their coniferous part by controlling their competition, impacting the ecosystem C stocks. The aim of this study was to compare the short-term effect of motor-manual release treatments of variable intensities: broadcast brushing (brushing), release from below (RFB), and crop tree release on carbon stocks in soil (SOC) and live biomass (aboveground and root C stocks). The stands treated by brushing had twice as much SOC stocks than in the other stands, at the 5–10 cm depth. RFB treatment kept the largest aspen stems in the stands and retained enough total live biomass to compensate for the initial loss. Despite being similar in intensity, stands treated with RFB had more than twice the amount of live biomass than stands treated by brushing. Overall, total C stocks (SOC and live biomass) did not change between the stands but the distribution among the different C pools did, with the live biomass being the most impacted by the treatments. The design of the RFB treatment seemed promising to mitigate C loss during early forest operations while still controlling fast-growing competitors.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".