Genetic Worth Effect Models for Boreal Conifers and Their Utility When Integrated into Density Management Decision-Support Systems
Why this work is in the frame
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Bibliographic record
Abstract
Based on approaches deduced from previous research findings and empirical observations from density control experiments, genetic worth effect response models were developed for black spruce (Picea mariana (Mill) BSP.) and jack pine (Pinus banksiana Lamb.) plantations. The models accounted for the increased rate of stand development arising from the planting of genetically-improved stock through temporal adjustments to the species-specific site-based mean dominant height-age functions. The models utilized a relative height growth modifier based on known estimates of genetic gain. The models also incorporated a phenotypic juvenile age-mature age correlation function in order to account for the intrinsic temporal decline in the magnitude of genetic worth effects throughout the rotation. Integrating the functions into algorithmic variants of structural stand density management models produced stand development patterns that were consistent with axioms of even-aged stand dynamics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it