Remote Sensing Approach for Environmental Monitoring: Application of Sudbury, Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ENVI and ArcGIS software along with Landsat TM data were used to evaluate sustainability and environmental conservation efforts in Sudbury region, Ontario, Canada. The study adopted three phase analysis. First, the change in landscape from 1984 to 2007 was studied. In the second part the study area was analyzed for urban heat island phenomenon by comparing thermal changes in relation to vegetation changes. The last part dealt with observing change in water quality parameter. Findings of the study revealed that significant change has taken place in Sudbury during the study period. Post classification comparison method has quantified the change and presented the results in the form of a change matrix, also an increase in the reclaimed land and dense vegetation in 2007 was observed while a significant decrease in the built up and barren land was also evident. Thermal analysis results showed overall higher temperatures in 1984 while the thermal signatures of 2007 images showed characteristic of urban heat island where urban core of Sudbury had high temperatures while the rural and vegetative areas had low temperatures. The water quality analysis showed an increase in the levels of phosphorus and dissolved organic carbon (DOC) concentrations in lakes around Sudbury with the exception of Kelly Lake. The error analysis shown regression-derived phosphorus distribution maps were unreliable in this application, due to significant average error.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it