Bibliographic record
Abstract
We address the problem of finding the bounding contour of a lake in high-resolution satellite imagery. The proposed solution consists of a novel approach for probabilistic contour grouping when some prior (possibly rough) knowledge about the lake is known. Such knowledge is available, for example, from an existing GIS. The grouping process is based on a Bayesian approach and a constructive algorithm. We use four object cues that are calculated for each edge segment in the image, and four grouping cues between consecutive edges on the lake boundary. We derived statistical models for these cues by using data from a training set of lakes, for which an IKONOS image and NTDB (Canadian National Topographic Database) polygonal data are available, and for which the contours have been traced manually. We then test the algorithm on new lakes. For the latter, we obtained independent measurements of the actual contour by eight geomatics experts. These measurements are used for the evaluation of the results. A quantitative analysis of the results shows that our algorithm improved on the accuracy of the prior GIS models by an average of 41%. The accuracy of our algorithm is comparable to human expert accuracy.
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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 | 0.000 | 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) | 0.142 | 0.001 |
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".