Investigating the potential of GIMMS and MODIS NDVI data sets for estimating gross primary productivity in Harvard Forest
Bibliographic record
Abstract
Accurate estimation of CO2fluxes is significant for studying the interaction between the terrestrial biosphere and the atmosphere, which is also highly relevant to the climate-policy making. Gross primary productivity (GPP), defined as the overall rate of fixation of carbon through the process of vegetation photosynthesis, is the total influx of carbon into an ecosystem. Many remote sensing approaches based on light use efficiency (LUE) model have been developed to estimate GPP at regional or global scale. A standard suite of global products characterizing GPP at the 1km spatial resolution is now being produced operationally based on observations from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In this study, we investigated the potential of two normalized difference vegetation index (NDVI) data sets from global inventory modeling and mapping studies (GIMMS) and MODIS for estimating GPP in Harvard Forest. The result showed that only using NDVI and photosynthetically active radiation (PAR) can explain 74% of GPP for this site, which indicates GPP can be predicted by using long time period NDVI data sets at reasonable accuracy.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".