Use of change-point detection for friction–velocity threshold evaluation in eddy-covariance studies
Bibliographic record
Abstract
The eddy-covariance method often underestimates fluxes under stable, low-wind conditions at night when turbulence is not well developed. The most common approach to resolve the problem of nighttime flux underestimation is to identify and remove the deficit periods using friction–velocity ( u * ) threshold filters ( u * Th ). This study modifies an accepted method for u * Th evaluation by incorporating change-point-detection techniques. The original and modified methods are evaluated at 38 sites as part of the North American Carbon Program (NACP) site-level synthesis. At most sites, the modified method produced u * Th estimates that were higher and less variable than the original method. It also provided an objective method to identify sites that lacked a u * Th response. The modified u * Th estimates were robust and comparable among years. Inter-annual u * Th differences were small, so that a single u * Th value was warranted at most sites. No variation in the u * Th was observed by time of day (dusk versus mid or late night), however, a few sites showed significant u * Th variation with time of year. Among-site variation in the u * Th was strongly related to canopy height and the mean annual nighttime u * . The modified u * Th estimates excluded a high fraction of nighttime data – 61% on average. However, the negative impact of the high exclusion rate on annual net ecosystem production (NEP) was small compared to the larger impact of underestimating the u * Th . Compared to the original method, the higher u * Th estimates from the modified method caused a mean 8% reduction in annual NEP across all site-years, and a mean 7% increase in total ecosystem respiration ( R e ). The modified method also reduced the u * Th -related uncertainties in annual NEP and R e by more than 50%. These results support the use of u * Th filters as a pragmatic solution to a complex problem.
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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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".