Enhanced Climate-Sensitive Crop Planning Models for Multiple Criteria Decision-Making When Managing Jack Pine and Red Pine Forest Types
Bibliographic record
Abstract
For jack pine (Pinus banksiana Lamb.) and red pine (Pinus resinosa Aiton) forest types, the goal of this study was to develop and demonstrate enhanced climate-smart crop planning models that are capable of simultaneously addressing both conventional and evolving forest management objectives, i.e., volumetric yield, wood quality, carbon storage-based harvestable wood product (HWP) production, and biodiversity-driven deadwood accumulation objectives. Procedurally, this involved the following: (1) development and integration of species-specific cambial age prediction equations and associated integration of whole-stem fibre attribute prediction equation suites, previously developed for wood density (Wd), microfibril angle (Ma), modulus of elasticity (Me), fibre coarseness (Co), tracheid wall thickness (Wt), tracheid radial (Dr) and tangential (Dt) diameters, and specific surface area (Sa), into climate-sensitive structural stand density management models (SSDMMs); (2) modification of the computational pathway of the SSDMMs to enable the estimation of abiotic stem volume production; and (3) given (1) and (2), exemplifying the potential utility of the enhanced SSDMMs in operational crop planning. Analytically, to generate whole-stem attribute predictions and derive HWP estimates, species-specific hierarchical mixed-effects cambial age models were specified, parameterized, and statistically validated. The previously developed attribute equation suites along with the new cambial age models were then integrated within the species-specific SSDMMs. In order to facilitate the calculation of accumulated deadwood production arising from density-dependent (self-thinning) and density-independent (non-self-thinning) mortality, the computational pathways of the SSDMMs were augmented and modified. The utility of the resultant enhanced SSDMMs was then exemplified by generating and contrasting rotational volumetric yield, wood quality attribute property maps, quantity and quality (grade) of solid wood and non-solid wood HWPs, and deadwood production forecasts, for species–locale–RCP-specific crop plan sets. These analytical model-based innovations, along with the crop planning exemplifications, confirmed the adaptability and potential utility of the enhanced SSDMMs in mitigating the complexities of multiple criteria decision-making when managing jack pine and red pine forest types under climate change.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".