Testing the performance of a forest ecosystem model (FORECAST) against 29 years of field data in a<i>Pseudotsuga menziesii</i>plantation
Bibliographic record
Abstract
The ability of the forest ecosystem management model FORECAST to project a 29-year record of stand response to factorial thinning and fertilization treatments in a Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) plantation at Shawnigan Lake (Vancouver Island, British Columbia, Canada) was assessed. Model performance was evaluated firstly using for calibration a regional data set and secondly with site-specific data from control plots. Model output was compared against field measurements of height, diameter, stem density, component biomass (aboveground), and litterfall rates and estimates of nutrient uptake, foliar N efficiency, and understory vegetation biomass. When calibrated with regional data, results from graphical comparisons, three measures of goodness-of-fit, and equivalence testing demonstrated that FORECAST can produce predictions of good to moderate accuracy depending on the variable of interest. Model performance was generally better when compared with field measurements (e.g., top height, diameter at breast height, and stem density) as opposed to outputs derived from allometric and volume equations. Use of site-specific data to calibrate the model always improved performance, although improvements were modest for most variables, with the exception of branch and foliage biomass. The benefits of site-specific calibration, however, should be weighed against the costs of obtaining such data. The intended use of the model will likely determine the level of effort expended in its calibration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".