Analysis of the Canadian boreal forest using enhanced resolution ERS-1 scatterometer imagery
Bibliographic record
Abstract
Scatterometer backscatter measurements (/spl sigma//spl deg/) are primarily and traditionally used to estimate wind speed and direction over the ocean. This paper presents an investigation of the backscatter coefficient of boreal forest and neighboring vegetation regions. ERS-1 backscatter A-values of the Canadian boreal forest are imaged using a resolution enhancement algorithm for this analysis. Regions of boreal forest, tundra, and grassland are individually analyzed over the extent of the ERS-1 scatterometer's data set (1992-1995). The annual variation of the mean /spl sigma//spl deg/ value for each region is presented. Distinct seasonal variations exist for these vegetation types. Boreal forests exhibit a stronger response (/spl sim/2.5 dB) during warm summer months than during the snow and ice covered winter months. The results of this study indicate good potential for further analysis of boreal forest regions using ERS-1 scatterometer data.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".