Maximum heat ratio: bi-directional method for fast and slow sap flow measurements
Bibliographic record
Abstract
Abstract Background As sap flow research expands, new challenges such as fast sap flows or flows co-occurring with freeze/thaw cycles appear, which are not easily addressed with existing methods. In order to address these new challenges, sap flow methods capable of measuring bidirectional, high and slow sap flux densities (Fd, cm3cm−2 h−1), thermal properties and stem water content with minimum sensitivity to stem temperature are required. Purpose In this study we assessed the performance of a new low-power ratio-based algorithm, the maximum heat ratio (MHR) method, and compare it with the widely known heat ratio (HR) method using a cut-tree study to test it under high flows usingEucalyptus grandistrees, and a freeze/thaw experiment usingAcer saccharumtrunks to test its response to fast changing stem temperatures that result in freeze/thaw cycles. Results Our results indicate that MHR and HR had a strong (R2 = 0.90) linear relationship within aFdrange of 0–45 cm3 cm−2 h−1. Using the MHR algorithm, we were able to estimate wood thermal properties and water content, while extending the measuring range of HR to approximately 0–130 (cm3cm−2 h−1). In our freeze/thaw experiment, the main discrepancy between MHR and HR was observed during freezing, where HR had consistently lowerFd(up to 10 cm3 cm−2 h−1), with respect to MHR. However, both algorithms identified similar zero flows. Conclusion Consequently, MHR can be an easy-to-implement alternative algorithm/method capable of handling extreme climatic conditions, which can also run simultaneously with HR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".