ExoGIS: An internet-based geographic information system in support of planetary science
Bibliographic record
Abstract
The Canadian Space Agency (CSA) actively supports research projects in the field of planetary sciences that are carried out through the Canadian Analogue Research Network (CARN). CARNd projects provide the background data used in comparative studies of the Earth, Moon, and Mars. These studies require sophisticated tools for the visualization, manipulation, analysis, and interpretation of geospatial terrestrial and planetary databases. The large amount of data, gathered through current and future missions, must be managed for easy access by principal investigators, CSA staff, and stakeholders from government, universities, industry, and other space agencies. We are currently developing an internet-based geographic information system (WebGIS) that has three objectives: (i) to promote and facilitate research at analogue sites in Canada and other locations where CSA field deployments are carried out; (ii) to forge stronger links with the international Earth and planetary science community by sharing geospatial information; and (iii) to give visibility to the CSA in the field of analogue and planetary GIS. We propose building a WebGIS architecture according to the international standards developed by the Open Geospatial Consortium for terrestrial data, and the International Planetary Data Alliance for planetary bodies. To illustrate the versatility of WebGIS applications, we use an example based on the composition of lunar rocks produced from datasets acquired by Lunar Prospector and extracted from the Planetary Data System.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".