Eelgrass Extent - Coastal British Columbia
Bibliographic record
Abstract
This dataset represents polygons of eelgrass on the BC coast predicted from existing geospatial datasets of seagrass extent (ShoreZone) and bathymetry. The extent of eelgrass in British Columbia was estimated using a novel tool created for ArcGIS (the Gregrator).This tool requires 2 inputs: (1) the coastline data from Shorezone (including line shapefiles that represent the extent of eelgrass) and (2) bathymetry data. Briefly, each line segment of coast in ShoreZone is converted to points along its vertices. These points are used to generate Thiessen polygons, where each Thiessen polygon “defines an area of influence around its sample point, so that any location inside the polygons is closer to that point than any of the other sample points” (Esri, 2016). Thiessen polygons were then clipped to the extent of polygons which spanned the area from the coastline to maximum depth based on the results of the bathymetry analysis. These methods are based on those described in Gregr et al 2013. Gregr, E. J., Lessard, J., & Harper, J. (2013). Progress in Oceanography A spatial framework for representing nearshore ecosystems. Progress in Oceanography, 115, 189–201. http://doi.org/10.1016/j.pocean.2013.05.028 Contact data@hakai.org for more information
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.054 |
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".