Investigating Labrador Fens and Bogs using Multi-Temporal ERS-2 and RADARSAT Data
Bibliographic record
Abstract
Bog and fen wetland complexes comprise a large percentage of ground cover in central Labrador and contain some of the largest peatlands in North America. The region experiences long cold winters and short cool summers, resulting in a limited growth period. The level of moisture saturation, chemistry, topography and climate influences the development of wetland systems. Consequently, slight changes in these environmental factors can significantly alter vegetation species and health. As persistent cloud cover often limits the utility of optical data in Atlantic Canada, the value of using the all-weather capabilities of radar data is evident. Temporal sequences of Radarsat images (C-HH) were acquired in May, June and August 1999, during which four Radarsat scenes, with incidence angles spanning 20-49° (Standard 1, 4, 7 and Fine 1) were acquired for each time period. Throughout the study period, 6 ERS-2 images (C-VV) were also acquired. This paper describes changes in radar backscatter as a function of incidence angle, vegetation structure and polarization.
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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) | 0.000 | 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 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".