Predicting aboveground biomass carbon sequestration potential in hybrid poplar clones under afforestation plantation management in southern Ontario, Canada
Bibliographic record
Abstract
Afforestation systems as a pathway for natural climate solutions contributing to terrestrial C sequestration are influenced by agroclimatic conditions, tree species and clones. This study validated a regression equation to predict aboveground biomass C (AGBC) sequestration potentials of hybrid poplar clones under afforestation plantation and compared these clones’ adaptability to three levels of land suitability in four afforestation sites in southern Ontario, Canada. Results validated the proven ability of the GenOnBio model to reasonably predict AGBC content in all tested clones. This research suggests that DN154 and FFC1 having C sequestration rates of 2.19 and 2.13 Mg C ha-1 y-1, respectively, are suitable for marginal lands having high land suitability condition. In contrast, DTAC29, and DTAC26 (0.56 and 0.88 Mg C ha-1 y-1, respectively) should not be selected for the above land suitability. On marginal lands with severe limitations, NM6 (1.53 Mg C ha-1 y-1) showed the highest adaptability for AGBC sequestration. Our findings confirm that poplar afforestation on marginal lands in southern Ontario, at least up to the age of 15 years, can significantly contribute to AGBC sequestration, which in turn can have significant positive influence on the current 2 billion tree planting program initiated by the federal government.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".