Problems and options in modelling fine-root biomass of single mature Norway spruce trees at given points from stand data
Bibliographic record
Abstract
In a 75-year-old Norway spruce (Picea abies (L.) Karst.) stand, three different single-tree models were tested to predict the fine-root biomass of root samples. This approach is based on the assumption that the fine-root biomass at a given point determines the availability of belowground resources as, for example, soil water. All models assume a monotonously decreasing function describing the distribution of the fine-root biomass of a subject tree depending on the distance to the trunk of the tree. To the contrary, the models differ in the maximum distance from the stem where roots can be found. There were high correlations between the observed and the predicted fine-root biomasses for all models in a part of the stand where the trees are distributed less uniformly and where root biomasses are most heterogeneous. In a section with medium stand density, the model of diameter at breast height dependent root spread yields higher correlation coefficients compared with the fixed-distance approach of the two other models. Significant correlations between model predictions of root distributions and measured soil water potential supported the validity of the models. The results of the model estimations imply differences in the maximum distance of lateral root spread dependent on stand density.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| 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".