Bibliographic record
Abstract
The GIMMS NDVI dataset has been widely used to document a 'browning trend' in North American boreal forests (Goetz et al 2005, Bunn et al 2007, Beck and Goetz 2011). However, there has been speculation (Alcaraz-Segura et al 2010) that this trend may be an artifact due to processing algorithms rather than an actual decline in vegetation activity. This conclusion was based primarily on the fact that GIMMS NDVI did not capture NDVI recovery within most burned areas in boreal Canada, while another dataset consistently showed post-fire increasing NDVI. I believe that the results of Alcaraz-Segura et al (2010) were due simply to different pixel sizes of the two datasets (64 km2 versus 1 km2 pixels). Similar results have been obtained from tundra areas greening in Alaska, with the results simply due to these pixel size differences (Stow et al 2007). Furthermore, recent studies have documented boreal browning trends based on NDVI from other sensors. Beck and Goetz (2011) have shown the boreal browning trend derived from a different sensor (MODIS) to be very similar to the boreal browning trend derived from the GIMMS NDVI dataset for the circumpolar boreal region. Parent and Verbyla (2010) found similar declining NDVI patterns based on NDVI from Landsat sensors and GIMMS NDVI in boreal Alaska. Zhang et al (2008) found a similar 'browning trend' in boreal North America based on a production efficiency model using an integrated AVHRR and MODIS dataset.
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".