Evaluation of the Empirical Piecewise Regression Model in Simulating GPP in the Northern Great Plains
Bibliographic record
Abstract
An empirical piecewise regression (PWR) model was developed to estimate gross primary production (GPP) and improve the understanding of carbon fluxes for the grassland ecosystems in the Northern Great Plains. The PWR model spatially scales up the localized flux tower measurements across the grassland ecoregion at 1-km resolution. In this study, cross-validation was used to evaluate the robustness of the PWR model in the Northern Great Plains. The results showed that the PWR modeling approach was robust with a good agreement (index of agreement d = 0.71-0.97) between the PWR GPP and tower-measured GPP by withholding site, and a good agreement (d = 0.86-0.91) by withholding year. The PWR model was developed from five Northern Great Plain flux towers to predict GPP in the ecoregions. The PWR GPP and MODIS GPP were then compared with the tower-measured GPP. The results showed a good agreement of GPP among PWR, MODIS, and tower measurements at the Fort Peck, Mandan, and Cheyenne site. The MODIS GPP, however, did not agree well with the tower measurements at the Miles City and Lethbridge sites (d = 0.62-0.79). Differences between MODIS GPP and tower measurements at the Miles City and Lethbridge implied that the MODIS GPP failed to capture the seasonal dynamics of the growing season and locally overestimated or underestimated the tower-measured GPP at the two sites. Those discrepancies may be attributed to three potential problems: 1) pixel misregistration, 2) flux tower measurements, and 3) model estimates. The GPP spatial maps from the PWR and MODIS models were also compared for grasslands for the entire study area. The PWR GPP was lower than, or similar to, the MODIS GPP in the east and higher in the west and south. Environmental factors that may contribute to the spatial patterns of the GPP differences between the two models were then evaluated using a decision tree technique. The results of the decision tree analysis suggested that percentage of C4 grasses, soil water holding capacity, percentage of clay, and percentage of cropland mixed in the grassland contributed to the GPP difference patterns of the PWR and MO DIS models. Keywords: Carbon cycle; Decision tree; Gross primary production (GPP); MODIS GPP; Model evaluation; Northern Great Plains; Remote sensing
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".