The quantitative importance of stemflow:: An evaluation of past research and results from a study in Lodgepole Pine (Pinus contorta var. latifolia) stands in Southern British Columbia
Bibliographic record
Abstract
Stemflow is a focused point source input of precipitation and nutrients at the base of a tree or plant, and can have a significant impact on site hydrology. A review paper examining the quantitative importance of stemflow, and a stemflow modelling paper focused on juvenile lodgepole pine are presented in this thesis. Stemflow production information from 145 different studies is presented in table format with the addition of author-calculated funnelling ratios and plateau funnelling ratios when applicable. Plateau funnelling ratios were calculated to provide an estimation of the rainfall depth required to satisfy the storage capacity of a tree. Reference tables were used to identify inter-climatic, inter-genera, and intra-genera variations in stemflow production. Plateau funnelling ratios were used to identify shortcomings in current canopy interception models. Finally, the reference tables were used to identify areas of the stemflow literature where knowledge remains fairly weak. To date, no known studies have modelled stemflow production for juvenile lodgepole pine (Pinus contorta var. latifolia). Meteorological conditions, tree characteristics, and stemflow were sampled for two juvenile lodgepole pine stands over the course of the 2009 growing season. Step-wise multiple regression was used to assess which meteorological and tree architecture variables influenced stemflow production for each research plot. Once predictor variables were identified, models were produced for each stand and a generic model was produced that applied to both plots. A model employing precipitation depth and crown projection area successfully explained 71.3 % of the variation in stemflow production from sampled trees.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".