A Height Growth Model and Associated Growth Intercept Models for Estimating Site Index in Black Spruce (Picea mariana Mill. B.S.P.) Plantations in Northern Ontario, Canada
Bibliographic record
Abstract
Abstract Site quality is a key component of growth and yield models because height growth rates are known to be influenced by available site resources. Accurate prediction of future growth and yield requires site quality information for both plantations and natural stands. The forest industry in northern Ontario relies on high-quality wood and fiber from black spruce (Picea mariana Mill. B.S.P.); therefore, these tools are essential to ensure sustainable forest management. Although there are site index (SI) models for natural-origin black spruce stands in northern Ontario, models for estimating site quality of young black spruce plantations have not been developed. We used stem analysis data collected from 62 plantations (>40 years of age) of pure black spruce across northern Ontario to develop height growth, SI, and variable growth intercept models. The distinct height growth patterns we observed may be attributed to early silvicultural treatments (site preparation and herbicide) in plantations allowing black spruce trees to attain breast height (1.3 m) faster than in fire-origin stands in northern Ontario. Our models can be used to estimate site quality of black spruce plantations, a key consideration for silviculture and forest management planning. We also compare our managed stand SI model to one we developed from a comparable subset of data from black spruce growing in unmanaged stands and propose a method to assign an SI with a common base age to pure upland black spruce stands regardless of origin.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".