Evaluation of stand-level hybrid PipeQual model with permanent sample plot data of Norway spruce
Bibliographic record
Abstract
The evaluation of process-based models (PBM) includes ascertaining their ability to produce results consistent with forest growth in the past. In this study, we parameterized and evaluated the hybrid model PipeQual with datasets containing traditional mensuration variables collected from permanent sample plots (PSP) of even-aged Norway spruce (Picea abies (L.) Karst.) stands in Finland. To initialize the model in the middle of stand development and reproduce observed changes in Norway spruce crown structure, the built-in empirical relationships of crown characteristics were made explicitly dependent on the light environment. After these modifications, the model accuracy at the whole dataset level was high, with slope values of linear regressions between the observations and simulations ranging from 0.77 to 0.99 depending on the variable. The average bias ranged between −0.72 and 0.07 m in stand dominant height, –0.68 and 0.57 cm in stand mean diameter, –2.62 and 1.92 m2 in stand basal area, and 20 and 29 m3 in stand total stem volume. Stand dynamics after thinning also followed reasonable closely the observed patterns. Accurate predictions illustrate the potential of the model for predicting forest stand growth and forest management effects in changing environmental conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".