Lassoo!: an interactive graphical tool for seafloor classification
Bibliographic record
Abstract
Lassoo! is an interactive graphical tool that facilitates the empirical classification of seafloor materials. Lassoo! can: (1) input a multivariate geo-referenced data set (e.g. geophysical properties, acoustic data or acoustic derivative data sets); (2) display data in geo-referenced map space and/or in multivariate data space (e.g. side scan sonar imagery data in geo-referenced space and parameters derived from the imagery in bivariate space); (3) interactively select subsets of data points in either bivariate or geo-referenced spaces; (4) assign "classes" to selected regions in either data space and; (5) automatically identify these "classes" in both data spaces, Lassoo! has been used for the evaluation of data collected with the commercial sediment classification system "RoxAnn" in an area of seafloor dredge spoil dumping, The RoxAnn data were evaluated by direct comparison with side scan sonar data obtained with a Simrad EM1000 multibeam system. The purpose of the project was to monitor the dispersal of sediments from two dump sites in the survey area and to classify the various sediments encountered in this region. The results of acoustic classification of the sediments were compared to ground truth data obtained from cores. It was observed that the ability to select classes in one space and the identification of these classes in other spaces is a powerful tool for enhancing the quality of empirical sediment classification.
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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) | 0.003 | 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 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".