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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".