Modelling the Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) site index from site factors in Portugal
Bibliographic record
Abstract
Although the speed of growth and adaptability of the north-west American conifer Douglas-fir has been recognized in Portugal, it represents only ~0.1 per cent of the total forest cover. This small area is spread across the mountainous areas of the north and centre of the country. This study models Douglas-fir productivity based on site factors and estimates the potential area for Douglas-fir in Portugal. Soil, climate and topographic data were collected on 39 plots across the range of sites where Douglas-fir grows in Portugal. The analysis of the data followed three steps: (1) selection of site factors related to the variation in the site index; (2) choice of candidate models; and (3) consideration of the best model to explain and predict the site index from site factor variables. The best multiple linear regression model explained 90 per cent of site index variation but included variables not readily available in the field. A model using digitized site data explained 54 per cent of the variation in the site index and mapped the areas with potential for Douglas-fir growth in Portugal. It is estimated that a potential Douglas-fir planting area of 250 000 ha exists where trees will exceed 17 m dominant height at age 30 years. This would correspond to 8 per cent of the existing Portuguese forest area. The best sites for Douglas-fir growth are located along north coastal to central regions at altitudes between 500 m and 1000 m with moisture deficit (precipitation minus evapotranspiration) above 1000 mm. Areas with acceptable sites for Douglas-fir growth are located in the north and centre of Portugal at 700–1000 m elevation and have a moisture deficit above 400 mm
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".