Browning of the landscape of interior Alaska based on 1986-2009 Landsat sensor NDVI
Bibliographic record
Abstract
We used a time series of 1986–2009 Landsat sensor data to compute the Normalized Difference Vegetation Index (NDVI) for 30 m pixels within the Bonanza Creek Experimental Forest of interior Alaska. Based on simple linear regression, we found significant (p < 0.05) declining trends in mean NDVI of three dominant landscape types of floodplains, lowlands, and uplands. At smaller patch sizes, similar declining trends occurred among topographic classes of north- and south-facing slopes and valley bottoms and among forest classes, including black spruce (Picea mariana (Mill.) B.S.P.). Significant positive trends in mean NDVI occurred only in areas that were recently burned, whereas wetlands had no significant trend. The greatest departure from the NDVI trend line occurred following the 2004 drought for all forest classes except black spruce, which dominates the coldest sites, and balsam poplar (Populus balsamifera L.), which occurs on low, moist terraces within the Tanana River floodplain. The consistent long-term declining trend at several spatial scales may be due to a regional climatic regime shift that occurred in the mid-1970s.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".