Spatial mapping of growing degree days: an application of MODIS-based surface temperatures and enhanced vegetation index
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
Growing degree days (GDD) is a simple temperature-based index of biological development. In this paper we evaluated the potential of using 2003-2005 MODIS-based 8-day and 16-day composites of daytime surface temperature (TS) and enhanced vegetation index (EVI) values at 250 m resolution for mapping GDD. The work was applied to the Canadian Atlantic Maritime Ecozone as a demonstration of the methodology. The work proceeded by establishing an empirical relationship between mean tower-based estimates of TS for the MODIS-acquisition period of 10:30 am-12:00 pm and the daily mean TS calculated from half-hourly emitted infrared/longwave radiation measurements taken from four flux sites in southern commercial forests of Canada. The relationship revealed a strong correlation between variables (r2=98.4%) and was central to the calculation of daily mean TS from MODIS-based estimates of TS. Since seasonally-based estimates of GDD and EVI were strongly correlated (r2=87%), data fusion techniques were applied to enhance the GDD map originally produced at 1 km resolution (from infrared emission band data), to 250 m. In general, the MODIS-derived map of GDD showed a positive constant offset of about 511 degree days from calculated long-term averages (1971 2000) based on temperatures collected at 101 Environment Canada climate stations.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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