Modelling Canopy Actual Transpiration in the Boreal Forest with Reduced Error Propagation
Bibliographic record
Abstract
The authors have developed a scaling approach to aggregate tree sap flux with reduced error propagation in modeled estimates of actual transpiration () of three boreal species. The approach covers three scales: tree point, single tree trunk, and plot scale. Throughout the development of this approach the error propagated from one scale to the next was reduced by analyzing the main sources of error and exploring how some field and lab techniques, and mathematical modeling can potentially reduce the error on measured or estimated parameters. Field measurements of tree sap flux at the tree point scale are used to obtain canopy transpiration estimates at the plot scale in combination with allometric correlations of sapwood depth (measured microscopically and scaled to plots), sapwood area, and leaf area index. We compared the final estimates to actual evapotranspiration and actual transpiration calculated with the Penman–Monteith equation, and the modified Penman–Monteith equation, respectively, at the plot scale. The scaled canopy transpiration represented a significant fraction of the forest evapotranspiration, which was always greater than 70%. To understand climate change impacts in forested areas, more accurate actual transpiration estimates are necessary. We suggest our model as a suitable approach to obtain reliable estimates in forested areas with low tree diversity.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".