Hunter Island Hauyat Village Site Elevation Point Data - British Columbia - Canada
Bibliographic record
Abstract
This dataset is a bare earth model of the Hauyat Site on Hunter Island, British Columbia. The project was led by Dana Lepofsky of Simon Fraser University, and was run in partnership with the Hakai Institute and the Heiltsuk Integrated Resource Management Department (HIRMD). Morgan Ritchie (morganritchie@yahoo.com) led a mapping team that collected 935 points with a total station in 2015. The goal of the 2016 mission was to fill in the total station data, as well as to fill out the "wings" of the site. The 2016 mission took place from June 4th-9th, 2016, at the Hauyat site on north Hunter Island. The Hakai Institute field team (Keith Holmes and Will McInnes), did an RTK GPS survey (with a Topcon GR-5 RTK setup). We set up a base station on "Plum Island", the island in the middle of the bay in front of the Hauyat site. We collected elevation points with the RTK, and we also collected burm/house features and rock locations. Because of the challenges of the site for a GPS system - dense trees and north facing - the RTK GPS could not obtain a fix in the back part of the site. We therefore also used a laser rangefinder to fill in some areas where there was no total station data and the RTK GPS could not collect points. The survey for all data collection types was set up in Nad83 UTM9N, based on the datum of the RTK GPS. The total station and rangefinder data was therefore corrected to fit with the RTK data. Contact data@hakai.org for access to data. Ensure Dana Lepofsky, dlepofsk@sfu.ca, is consulted before access.
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 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 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| 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) | 0.028 | 0.013 |
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".