X' Marks the Spot: Transferring Dig Site Coordinates from Maps to Google Earth
Bibliographic record
Abstract
Dinosaur Provincial Park has been a popular site for palaeontological digs for many years. Over time, the many quarries and bone beds uncovered have had their locations marked on large paper topography maps. Unfortunately, many dig sites have been lost due to poor documentation. Some sites have been abandoned for years. The high erosion levels of the park (2 – 4 mm yearly) continually both destroys dig sites and uncovers new fossils. To help recover old, unused dig sites, the coordinates of the sites marked on the old paper maps were uploaded to Google Earth Pro for easy access. Unfortunately, the points had to first be transferred to clear mylar maps, because the original paper maps lacked longitude and latitude measurements. This was accomplished by matching the topography when the scale of the maps differed, and by overlaying the clear maps on the paper maps when they did not. The distance of each point from a line of longitude or latitude was found using a ruler (each mm measured on the map representing 10 m in the park) and used to calculate their coordinates. After the coordinates were found, they were recorded in a Google SpreadSheet. Once this was completed for all 462 points, they were uploaded to Google Earth Pro. The purpose of this project was to provide more easily accessible records of dig sites and prevent further record loss as the old paper maps age and their condition deteriorates. The massive paper maps are unwieldy and impractical to use in the field, and something more compact is needed. Google Earth is easily accessed on a computer or cell phone, and the points will not be lost due to physical damage, degradation, or misplacement of the records. In addition, it takes up far less space in digital form, and thus is better for field work than the original maps.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.057 |
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".