Landslides Kosñipata Valley, Peru (Clark et al. 2016)
Bibliographic record
Abstract
Please cite: Clark, K. E., West, A. J., Hilton, R. G., Asner, G. P., Quesada, C. A., Silman, M. R., Saatchi, S. S., Farfan Rios, W., Martin, R. E., Horwath, A. B., Halladay, K., New, M., and Malhi, Y. (2016), Storm-triggered landslides in the Peruvian Andes and implications for topography, carbon cycles, and biodiversity, Earth Surface Dynamics, 4, 47-70, doi: 10.5194/esurf-4-47-2016. Landslides within the Kosñipata Valley in Peru were manually mapped over a 25-year period from 1988 to 2012 using Landsat 5 (Landsat Thematic Mapper) and Landsat 7 (Landsat Enhanced Thematic Mapper Plus) satellite images. The landslide inventory was produced by manually mapping landslide scars and their deposits in ArcGIS and by verifying via ground truthing of scars in the field. Mapping involved visually comparing images from one year to the next, specifically evaluating contrasting colour changes that suggest a landslide had occurred. The landslide areas visible via spectral contrast in the Landsat images include regions of failure, run-out areas, and deposits. Pan-sharpened high-resolution Quickbird and Worldview images were used to define the landslide boundaries. Topographic shadow produced by hillslopes covered a minimum of 21% of the study area (35 km2 out of 185 km2), predominantly on southwest-facing slopes was consistently present between images. Landslides that fell within these shadow areas were not visible. Any landslides that were partially mapped underneath the Landsat topographic shadow were removed (see Figure 2a in Clark et al. 2016). This product was created by Kathryn Clark (kathryn.clark23@gmail.com). Other spatial datasets from Clark et al. (2016): Clark, K., J. West, R. Hilton (2017). Landsat topographic shadow, Kosñipata Valley, Peru (Clark et al. 2016), HydroShare, http://www.hydroshare.org/resource/bdb9c4b4788d4141845947c81e5cceba Clark, K., J. West, R. Hilton (2017). Region of landslide mapping, Kosñipata Valley, Peru (Clark et al. 2016), HydroShare, http://www.hydroshare.org/resource/c08742b733274f7dbf75891a7c185626 Clark, K., J. West, R. Hilton (2017). Landslide rates and hillslope turnover, Kosñipata Valley, Peru (Clark et al. 2016), HydroShare, http://www.hydroshare.org/resource/147e9ebecde442ed97738de7f404c057
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".