Evaluation of Weibull-based parameter prediction equation systems for black spruce and jack pine stand types within the context of developing structural stand density management diagrams
Bibliographic record
Abstract
The objective of this study was to evaluate the predictive ability of Weibull-based parameter prediction equation (PPE) systems developed for natural (density unregulated) and managed (density regulated) black spruce (Picea mariana (Mill.) BSP) and jack pine (Pinus banksiana Lamb.) stand types (n = 6), using (1) seemingly unrelated regression (SUR) employing a recursive system specification, (2) cumulative density function regression (CDFR) in which the location parameter was estimated indirectly employing a minimum diameter function (denoted CDFR(1)), and (3) CDFR in which the location parameter was estimated directly from stand-level variables (denoted CDFR(2)). An Ontario-based calibration data set consisting of diameter frequency distributions and associated stand-level variables derived from 1591 permanent sample plot (PSP) measurements was used to develop SUR-based, CDFR(1)-based, and CDFR(2)-based PPE systems. The calibration data set and an Ontario-based independent test data set, which consisted of stand-level variables and associated diameter frequency distributions derived from 244 PSP measurements, were used to evaluate the resultant PPE systems. Based on the approximate equivalency among the PPE systems in terms of (1) goodness-of-fit indices, (2) lack-of-fit statistics, (3) prediction error indices, and (4) stand-level product value prediction error, all three parameterization methods were found to be of equal utility, irrespective of stand type.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".