Applying time series models to estimate time lags between sap flux and micro-meteorological factors
Bibliographic record
Abstract
Sap flux (Ft) measurements are extensively used to scale-up canopy transpiration and conductance, but time lag between sap flux and canopy transpiration is a problem. As canopy transpiration is nearly synchronous with micrometeorological drivers, better understanding of the lag relationships between Ft and micrometeorological drivers and soil water conditions would benefit the up-scaling of canopy transpiration from sap flux. Time series modeling at different spatial and temporal scales can identify and incorporate time lag effects, as well as multiple variables affecting transpiration and the interactions between them. SARIMAX and GARCH hybrid models were used to capture seasonality and autoregressive conditional heteroscedasticity effects. Two univariate hybrid models were designed to measure vapor pressure deficit () and photosynthetic active radiation (), and one multivariate hybrid model was used in each season. Sap flow lagged behind canopy transpiration by 0–10 min in the dry season and 10–20 min in the wet season. had a stronger influence on transpiration than . Only the interaction between and in the wet season was observed. This study extends the application of time series modeling to the prediction of sap flow dynamics.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".