Analysis of land use and land cover change in Kiskatinaw River Watershed: A remote sensing, gis & modeling approach.
Bibliographic record
Abstract
This thesis study was conducted to capture the land use and land cover (LULC) change dynamics in Kiskatinaw River Watershed, BC, Canada. A combination of remote sensing, GIS and modeling approach was utilized for this purpose. Landsat TM and ETM+ satellite images of the years 1984, 1999 and 2010 were analyzed using object oriented image classification technique to produce LULC maps and detect the associated changes. The dynamic nature of different forest types, increase in built-up area and significant depletion of wetlands were found to be notable among the detected LULC changes. Thereafter, a multi-layer perception neural network technique was used to model transition potentials of various LULC types, which was later realized with a Markov Chain land use model to predict future changes. The integration of advanced satellite remote sensing tools and neural network aided Markov Chain modeling was illustrated to be an effective means for LULC change detection and prediction in Kiskatinaw River Watershed. --Leaf ii.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".