Broad‐scale satellite <scp>N</scp>ormalized <scp>D</scp>ifference <scp>V</scp>egetation <scp>I</scp>ndex data predict plant biomass and peak date of nitrogen concentration in <scp>A</scp>rctic tundra vegetation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Questions Is the satellite‐derived N ormalized D ifference V egetation I ndex ( NDVI ) an adequate proxy for the timing of the peak in plant nitrogen concentration in an A rctic tundra system? Can NDVI be used to reliably assess seasonal changes in aboveground plant biomass? Location The south plain of B ylot I sland, an A rctic tundra ecosystem north of B affin I sland, N unavut, C anada (73°08′ N , 80°00′ W ). Methods Using plant data collected every 2 wk throughout the summer in 1991, 1993–1996 and 2006–2008, we assessed the relationship between four NDVI indices ( AVHRR satellite data at 1‐km 2 spatial resolution) and the date of peak nitrogen concentration in wetland graminoid plants, which represents seasonal variability in plant quality. We also examined the relationship between NDVI and the seasonal changes in aboveground live plant biomass. Results Three out of the four NDVI metrics that we tested were significantly related to date of peak nitrogen concentration. The strongest relationship was found with the date at which NDVI values reached 50% of their annual maximum ( r 2 = 0.87). We also found a positive exponential relationship between NDVI and aboveground biomass of plants ( r 2 = 0.58), though this relationship was strongest early in the growing season. Conclusions NDVI can be used as a proxy to determine date of peak nitrogen concentration in some tundra plants, and can thus be a reliable measure of the yearly changes in the timing of the availability of high quality food for herbivores. To a lesser extent, NDVI can also be used to assess seasonal change in plant biomass. This study provides additional support for the use of broad‐scale satellite‐derived NDVI to assess seasonal changes in habitat quality for herbivores.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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