Angular normalization of GOME‐2 Sun‐induced chlorophyll fluorescence observation as a better proxy of vegetation productivity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Sun‐induced chlorophyll fluorescence (SIF) has been regarded as a promising proxy for gross primary productivity (GPP) over land. Considerable uncertainties in GPP estimation using remotely sensed SIF exist due to variations in the Sun‐satellite view observation geometry that could induce unwanted variations in SIF observation. In this study, we normalize the far‐red Global Ozone Monitoring Experiment‐2 SIF observations on sunny days to hot spot direction (SIF h ) to represent sunlit leaves and compute a weighted sum of SIF (SIF t ) from sunlit and shaded leaves to represent the canopy. We found that SIF h is better correlated with sunlit GPP simulated by a process‐based ecosystem model and SIF t is better correlated with the simulated total GPP than the original SIF observations. The coefficient of determination ( R 2 ) are increased by 0.04 ± 0.03, and 0.07 ± 0.04 on a global average using SIF h and SIF t , respectively. The most significant increases of the R 2 (0.09 ± 0.04 for SIF t and 0.05 ± 0.03 for SIF h ) appear in deciduous broadleaf forests.
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.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it