Yield prediction errors of a stand density management program for black spruce and consequences for model improvement
Bibliographic record
Abstract
The objectives of this study were to (i) quantify the prediction error associated with estimating density (N (stems/ha)), quadratic mean diameter (Dq (cm)), basal area (G (m2/ha)), total volume (Vt (m3/ha)), and merchantable volume (Vm (m3/ha)) using a stand density management decision-support program (SDMDSP) developed for black spruce (Picea mariana (Mill.) BSP) plantations and (ii) given objective i, assess model adequacy by examining the relationship between prediction error and model input variables (prediction period, site index, initial density, and number of thinning treatments) by yield variate. Specifically, the SDMDSP was evaluated by comparing its yield predictions with corresponding measured values (n = 44) within 19 black spruce plantations. The resultant tolerance intervals indicated that 95% of the relative errors associated with future predictions would be within the following limits 95% of the time (minimummaximum): (i) 27.3 to 29.7% for N, (ii) 26.1 to 14.3% for Dq, (iii) 48.3 to 26.1% for G, (iv) 64.3 to 37.7% for Vt, and (v) 87.0 to 73.0% for Vm. Graphical analysis indicated that errors for Vt and Vm were associated with the data from thinned plantations. This result is discussed within the context of residual stand structure variation and response delay from which recommendations for model improvement are derived.
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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 | 0.003 | 0.006 |
| 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 | 0.000 |
| 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 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".