Estimating low and reverse sap flux density with the temperature ratio heat pulse (TRHP) method
Bibliographic record
Abstract
• A temperature ratio heat pulse (TRHP) method for determination of low and reverse sap flow. • The reverse sap flux density can be measured as low as −10.4 cm 3 cm -2 h −1 . • The method can accurately monitor nocturnal sap flow. The compensation heat pulse (CHP) method is widely-used for monitoring sap flux density, but it has a limited measurement range and often underestimates daily transpiration in plants. Here we present a temperature ratio heat pulse (TRHP) algorithm for extending the measurement range of the CHP method to low and reverse sap flows. With a similar probe configuration to CHP, TRHP utilizes the ratio of temperature changes associated with downstream and upstream probes (Δ T down /Δ T up ) for sap flow measurements. We verified the method through sand column and field employment. Sand column experiments show that the TRHP method successfully measured low and reverse flows, and the measured reverse sap flux density can be as low as −10 cm 3 cm -2 h −1 . Field deployments show that TRHP can monitor nocturnal sap flow more accurately than CHP method. This study thereby extends the capabilities of the CHP method for measuring low and reverse sap flows, which has wide applications in monitoring plant species where low and reverse sap flows are prevalent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".