Stem profile equations for young trembling aspen in northern Ontario
Bibliographic record
Abstract
Stem profile equations were developed for young trembling aspen (Populus tremuloides Michx.) growing on 6 different sites in northern Ontario. The objective was to develop a model using stem diameter at any height to predict stem diameter at other heights and then compare the model among sites. The 262 sample trees were split into calibration (180) and validation (82) sets. Four stem profile models having from 1 to 3 parameters were tested for parameter significance for each site, and then compared among sites. Data analysis was performed using bootstrap methods. One of the 4 models was not significantly different (P > 0.278) among sites. Validation of this model produced mean prediction errors and mean relative prediction errors of less than 0.001 cm and 0.09%, respectively; standard deviations of these errors were 0.15 cm and 4.09%, respectively. The presented method can be used to develop stem profile equations that are applicable to a broad range of growing conditions.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".