20m Digital Elevation Model - Calvert Island
Bibliographic record
Abstract
This DEM has been created from Hakai's Master Terrain Dataset (MTD) by means of the “Terrain to raster” tool in ESRI's ArcGIS for Desktop using a Natural Neighbour sampling method. The DEM has been natively created at 20m resolution. This DEM has been clipped to the shoreline of the island. A combination of different elevations around the island have been used to create the shoreline. This DEM's grid is lined up or "snapped" to the raster grid of the Vegetation Canopy Metrics and associated rasters. The resulting DEM is a bare earth, hydro-flattened elevation model and therefore considered "topographically complete". Each pixel represents the elevation in meters above average sea level of the bare earth at that location. The vertical reference system is "Canadian Geodetic Vertical Datum 1928" (CGVD28). Hakai has produced DEM's at different resolutions natively directly from the LiDAR data MTD. Please use the appropriate resolution product from those produced by Hakai for your research purposes. In order to maintain homogeneity, up-sampling / up-scaling from higher resolution products is not recommended as it may introduce and propagate errors of varying magnitudes into the analyses being conducted; please use products already available, and if you require a resolution not available contact data@hakai.org in order to obtain a DEM produced directly from the MTD. The following Topographically Complete DEMs have been produced natively from the DTM by Hakai: 3m Topographically Complete DEM. This product has been used to produce Hakai's hydrologic datasets (Streams and Watersheds) 20m Topographically Complete DEM. Compatible with Hakai's Vegetation Canopy Metrics and associated rasters. 25m Topographically Complete DEM. Compatible with BCGov TRIM data products. 30m Topographically Complete DEM. Compatible with STRM products. Master Terrain Dataset Creation: LiDAR point clouds from missions flown on 2012 and 2014 over Calvert Island where loaded (XYZ only) into a point feature class in an ESRI Geodatabase. Only ground (Class 2) returns where loaded onto the geodatabase. ESRI “terrain dataset” was created within the same geodatabase using the LiDAR points as embedded masspoints. TEM Plus lakes and ponds with average elevation values over the waterbodies’ mirrors were used as hard replace breaklines to achieve hydro-flattening. The minimum bounding geometry of all contiguous LAS files’ extents was used as a soft clip mask in the creation of the terrain dataset as project boundary. The horizontal coordinate system and datum employed for the terrain dataset is: UTM Zone 9 NAD1983; the vertical reference system was set to CGVD28. Both reference systems correspond to the native reference system of the LiDAR point clouds. The minimum point spacing defined during terrain dataset building was set to 1.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.167 |
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; both teacher heads agree on what is shown here.
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".