A closer look at site index - biogeoclimatic site series correlations: Douglas-fir in the Coastal Western Hemlock Zone, xm2 variant, 01 site series
Bibliographic record
Abstract
The Site Index - Biogeoclimatic Ecosystem Classification (SIBEC) model predicts site index from species and biogeoclimatic site series. The purpose of this project was, for Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco var. menziesii) in the CWHxm2/01 site series, to: 1) verify that the current SIBEC site index estimate of 32.90 m is accurate, 2) create a more site-specific SIBEC model by including additional variables in the model to capture within-site series variation, and 3) compare the SIBEC site index estimates and their variability when the estimates are obtained using stem analysis data versus data from Bruce’s (1981) height-age model. The data set consisted of 40 Douglas-fir plots established in the CWHxm2/01 site series. The analysis shows that the current SIBEC site index estimate should be changed to 34.68 m. No relationship was found between site index and ecological variables within this site series, hence the model could not be made more site-specific. The BEC system aggregates sites into relatively homogeneous units with respect to productivity. The comparison of the SIBEC site index estimates and their variances when estimated using stem analysis and a height-age model were not statistically different. Obtaining a site index estimate from Bruce’s model is as accurate as obtaining a site index estimate from stem analysis when the Douglas-fir sample trees are between 50 and 80 years of age.Key words: Biogeoclimatic Ecosystem Classification, Douglas-fir, model error, site index, site series, stem analysis
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".