Quantification of uncertainties in conifer sap flow measured with the thermal dissipation method
Bibliographic record
Abstract
Summary Trees play a key role in the global hydrological cycle and measurements performed with the thermal dissipation method ( TDM ) have been crucial in providing whole‐tree water‐use estimates. Yet, different data processing to calculate whole‐tree water use encapsulates uncertainties that have not been systematically assessed. We quantified uncertainties in conifer sap flux density ( F d ) and stand water use caused by commonly applied methods for deriving zero‐flow conditions, dampening and sensor calibration. Their contribution has been assessed using a stem segment calibration experiment and 4 yr of TDM measurements in Picea abies and Larix decidua growing in contrasting environments. Uncertainties were then projected on TDM data from different conifers across the northern hemisphere. Commonly applied methods mostly underestimated absolute F d . Lacking a site‐ and species‐specific calibrations reduced our stand water‐use measurements by 37% and induced uncertainty in northern hemisphere F d . Additionally, although the interdaily variability was maintained, disregarding dampening and/or applying zero‐flow conditions that ignored night‐time water use reduced the correlation between environment and F d . The presented ensemble of calibration curves and proposed dampening correction, together with the systematic quantification of data‐processing uncertainties, provide crucial steps in improving whole‐tree water‐use estimates across spatial and temporal scales.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 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".