The marginal cost of structural diversity of mixed uneven-aged hard maple forests
Bibliographic record
Abstract
Theoretical concepts pertaining to the marginal cost (MC) of the structural diversity of a forest stand are developed. A matrix growth model is estimated for mixed uneven-aged forest stands of hard maple (Acer saccharum Marsh.) from southern Ontario. The estimated growth model is used to derive the MC equations for the Shannon and the Simpson indices of total structural diversity (TSD), species diversity (SD), and tree-size diversity (TD). The effects of exclusion and inclusion of the opportunity cost (OC) on the MC of the TSD are compared. The contributions of SD and TD to the MC of the TSD are disaggregated. The MCs of TSD, SD, and TD for the Shannon and the Simpson indices are iso-elastic. The elasticity of the MC of the TSD for the inclusion of OC is greater than the elasticity of MC of the TSD for the exclusion of OC. The elasticities of MC of TSD, SD, and TD for the Shannon index are greater than the elasticities of MC of TSD, SD, and TD, respectively, for the Simpson index. The elasticities of MC of SD are smaller than the elasticities of MC of TD, for both indices. However, these results are specific to the hard maple forests of southern Ontario and cannot be generalized. Some general features of MC equations of structural diversity are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".