<i>Picea abies</i>site index prediction by environmental factors and understorey vegetation: a two-scale approach based on survey databases
Bibliographic record
Abstract
Relationships between site index, environmental variables, and understorey vegetation were examined for Norway spruce (Picea abies (L.) Karst.) in the eastern part of France. The study area concerns all the native range of Norway spruce in France and the northeastern plains. The analysis is based on 2087 plots from the French National Forest Inventory database. The data measured on each plot cover topography, soil, geology, and vegetation. Additional environmental variables were estimated using two methods: climatic data estimated from a climatic model developed by Météo-France (AURELHY), and nutritional variables predicted from vegetation data and species indicator values. General linear model regression was used to predict site index as a function of environmental variables. The best model explains 64% of the site index variance and involves eight variables (elevation, mountain zone, topographic concavity, proportion of plot area occupied by rock outcrop, rock type, soil depth, pH, and C/N ratio). The two main results of this study are (i) the combination of large databases allowed the study of soilsite relationships and construction of a pertinent model, which covers a wide range of ecological conditions, and (ii) vegetation was found to be relevant to separate the effect of acidity from those of nitrogen nutrition on Norway spruce productivity.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".