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
Bibliographic record
Abstract
Abstract Questions Is the satellite‐derived Normalized Difference Vegetation Index (NDVI) an adequate proxy for the timing of the peak in plant nitrogen concentration in an Arctic tundra system? Can NDVI be used to reliably assess seasonal changes in aboveground plant biomass? Location The south plain of Bylot Island, an Arctic tundra ecosystem north of Baffin Island, Nunavut, Canada (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‐km2 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 (r2 = 0.87). We also found a positive exponential relationship between NDVI and aboveground biomass of plants (r2 = 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.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".