GIS-Based Analysis and Modeling of Coastline Advance and Retreat Along the Coast of Guyana
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
This research utilized a Geographical Information System (GIS)-based approach to analyze, map and model coastline advance and retreat. A time series (1941–1987) of empirical advance and retreat data from the coast of Guyana was used. Coastlines were also extracted from 1987, 1990 and 1992 Landsat TM images, and 1999, 2002, 2004 and 2006 Landsat ETM+ images. The historical data were used to calculate advance and retreat (AOR) rates and sediment volume changes. Distinct periods of advance and retreat matched corresponding periods of sediment gains and losses. The Digital Shoreline Analysis System (DSAS) was used to predict rates of coastline change. Graphical plots of DSAS results identified spatial and temporal phase shifts of the coastline. Recurring episodes of accretion and erosion could be associated with the presence or absence of mudbanks along the coast.
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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 | 0.000 | 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 it