Geomorphology - Calvert Island - British Columbia - Canada
Bibliographic record
Abstract
Hakai Geomorphology - Polygon Features: Version 2 generated January 2015 by Jordan Eamer, Coastal Erosion and Dune Dynamics Laboratory at the University of Victoria. Reproduction requires explicit permission from the author: jeamer@uvic.ca This dataset was created for researchers at Hakai to utilize in instances where data on subsurface material type and relative age would be beneficial. It was created with a goal similar to other surficial geology maps in the region (e.g. http://www.env.gov.bc.ca/van-island/maps/surfical.jpg), with a focus on unconsolidated sediments deposited and reworked since the last glaciation. Data used in the generation of this map was collected through several different means. In areas with the highest resolution (e.g. proximal to the institute), subsurface data was directly observed through soil pits, natural sedimentary exposures, or cores. Where this was not possible, visual interpretation of a bare earth model generated from airborne lidar data assisted in extrapolation of geologic and landform trends that were directly observed. Derivatives of this model were also used, such as principal component analysis of hillshade models (e.g. Devereux et al., 2008) and a ruggedness index (Riley et al., 1999). Map data were created by Jordan Eamer and Dan Shugar. Devereux BJ, Amable GS, Crow P. 2008. Visualisation of LiDAR terrain models for archaeological feature detection. Antiquity 82: 470-479. Riley SJ, DeGloria SD, Elliot R. 1999. A terrain ruggedness index that quantifies topographic hetergeneity. Intermountain Journal of Science 5:23-27. This shapefile contains oriented linear and areal features identified through analysis of the DEM. These data may be used in a future study of glacial ice character and directionality. Last updated: March 26th, 2015. Map information currently undergoing publication and a citation will be added. Version 2 generated January 2015 by Jordan Eamer, Coastal Erosion and Dune Dynamics Laboratory at the University of Victoria.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.035 |
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".