100 Islands Project - Island Spatial Data -2017 - Coastal British Columbia - Canada
Bibliographic record
Abstract
This layer includes polygons representing the islands studied by the 100 Islands research program at the Hakai Institute. A centroid points layer is also included. The 100 Islands Project seeks to blend the theory of island biogeography with theories about ecosystem subsidies, to understand how nutrients from the sea affect the ecology of plants, breeding birds, mammals, insects and amphibians. To do this we will be studying 100 islands near the Hakai Institute on Calvert Island. Little is known of the diversity on these islands and therefore we have a great opportunity to conduct lots of interesting work, including mapping of major taxa, tracking marine-derived nutrients along shorelines (think seaweed deposition) and into the forests, and measuring stable isotope signatures in a variety of organisms (from tiny plants to large mammals), to name just a few! Attributes: Island general exposure as derived from the BC Shorezone dataset. Reflects the exposure on the more exposed side of each island. 6 classes (Very Exposed, Exposed, Semi Exposed, Semi Protected, Protected, Very Protected) Distance to Mainland over water in meters measured using the least distance of water crossing. Distance to nearest vegetated land. Radial coverage of land within 250m from the Island. (0 – 360°) i.e. the amount of surrounding land measured by the degrees of a circle it covers. Mean Normalized Difference Vegetation Index for the internal area of each island. NDVI is derived from WorldView2 images and available at its original 2m resolution. Dataset created by Wiebe Nijland and Luba Reshitnyk
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.010 |
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".