Optimizing high-dimensional forestry for wood production and carbon sinks
Bibliographic record
Abstract
Our model for optimizing stand-level wood production and carbon sinks includes individual-tree models for forest growth, an advanced model for soil carbon, detailed wood production economy, and an intertemporal objective for the value of wood production and carbon sinks/emissions. Carbon stocks include aboveground biomass and carbon in forest soil and in wood products. Optimization of the management regime, rotations and thinning timing, and type and intensity are carried out by reinforcement learning. Including the social price of carbon causes a regime switch from continuous cover forestry to clear-cuts, postponed lighter thinning, and a longer rotation, and with a high carbon price, to solutions with pure clear-cuts or solutions utilizing stands as pure carbon stocks. Carbon price has profound effects on stand values, and the bare land value may well exceed the value before a clear-cut. The total average carbon stock is maximized with harvest and a long rotation instead of “no harvesting”. Bioenergy, carbon capture, and storage (BECCS) always increases the value of wood production but not necessarily the value of carbon sinks. With BECCS, increasing carbon stocks in trees and forest soil remains optimal.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".