Characterizing the tundra taiga interface using Radarsat-2 (Mealy Mountains, Labrador)
Bibliographic record
Abstract
The transition zone between the boreal forest and Arctic tundra, also known as the \ntundra taiga interface (TTl), is a unique and sensitive ecosystem. A convenient way to \nmonitor and understand TTl changes is through the interpretation and analysis of earth \nobservation satellite images, or remote sensing. Ecosystem monitoring provides useful \ninformation about vegetation distribution and global climate. Currently, vegetation is \nmonitored at both global and regional scales through the use of multispectral, light \ndetection and ranging and synthetic aperture radar imagery. Each remote sensing \ntechnology offers unique spatial, spectral and radiometric resolution sets. \nThis thesis investigates the use of synthetic aperture radar images from the Canadian \nSpace Agency's RADARSAT-2 satellite to derive an image product discriminating \ndifferent types of vegetation cover within the TTl region of Labrador. \nA selection of texture measures was applied to a dataset consisting of six \nRADARSAT-1 and fourteen RADARSAT-2 images. Statistical parameters were utilized \nto measure how strongly the radar derived vegetation product correlated with the well established \nnormalized difference vegetation index (NDVI). The analysis was guided and \nvalidated by field data describing forest and non-forest land cover types. \nThe results indicate that a mean texture measure with a window size relating to a \nground area of 330x330 m (fine mode} and 450x450 m (standard mode) applied to an R-2 \nHV-polarized image is able to inform on the location of the TTl and also complements \nthe vegetation cover found in NDVl images.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".