The influence of site tree selection method on site index determination and yield prediction in black spruce stands in northeastern Québec
Bibliographic record
Abstract
Site index is a common and convenient indicator of forest site productivity. The concept is well suited for growth and yield predictions, although there appears to be no universal consensus on the type or number of site trees needed for its application. We compared four methods for assessing site quality using data from black spruce (Picea mariana (Mill.) BSP) stands of northeastern Québec. Data were analysed with a univariate repeated measures analysis of variance design using the MIXED procedure of the SAS system. Significant differences were found between the method based on the mean height of the 100 largest trees per hectare and three other methods that calculate site index using information from average site trees (codominants and dominants) and an equation to estimate top height from stand level data. We concur with many others that using the mean height of the 100 largest trees per hectare is a more standard procedure than simple averages of codominant and dominant tree heights for site quality assessment and growth modelling. We recommend that the next yield table system developed in the province should be based on top height trees, instead of using average codominants and dominants and an equation to estimate dominant height. Key words: site index, top height, yield table, site trees
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".