Bibliographic record
Abstract
The Ocean Mapping Group has been collecting data in the Arctic since 2003 and there are approximately 2,000 basemaps. In the current online storage format used by the OMG, it is difficult to view the data and users cannot easily pan and zoom. The purpose of this research is to investigate the advantages of the use of Google Maps, to display the OMG's Arctic data. The map should should load the large Artic dataset in a reasonable time. The bathymetric images were created using software in Linux written by the OMG, and a step-by-step process was used to create images from the multibeam data collected by the OMG in the Arctic. The website was also created using Linux operating system. The projection needed to be changed from Lambert Conformal Conic (useful at higher Latitudes) to Mercator (used by Google Maps) and the data needed to have a common colour scheme. After creating and testing a prototype website using Google Ground overlay and Tile overlay, it was determined that the high resolution images (10m) were loading very slowly and the ground overlay method would not be useful for displaying the entire dataset. Therefore the Tile overlays were selected to be used within Google Maps. Tile overlays used for this project proved to be useful for large datasets because they cut the image into many different tiles and load only the part of the image (tile) within the map window bounds. 1.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".