Reply to the discussion by Olsen and Stuedlein on “Use of terrestrial laser scanning for the characterization of retrogressive landslides in sensitive clay and rotational landslides in river banks”Appears in Canadian Geotechnical Journal, <b>47</b>(10): 1164–1168.
Bibliographic record
Abstract
In their discussion about aerial laser scanning (ALS) and terrestrial laser scanning (TLS), Olsen and Stuedlein present some issues than we did not address in our paper (Jaboyedoff et al. 2009a). Our study deals with compact landslide bodies and we had to overcome many related problems, such as shadowing, vegetation, site accessibility, and safety issues. In contrast, Olsen and Stuedlein deal with linear, clean features (coastal cliffs), which enable a completely different way of working. Nonetheless, their remarks are relevant and they were not addressed in our paper because it was beyond the scope of our study. Studies on landslide volumes and mechanisms do not in general need a high accuracy, while it is crucial for landslide movement monitoring. In their discussion, Olsen and Stuedlein give some examples of TLS applications in landslide studies in addition to those highlighted in our article. Most of them relate to coastal erosion and rockfalls in cliffs, and only a few relate to landslides. For the sake of completeness of the literature overview, TLS has also been widely used for
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.035 | 0.038 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".