Geographic information system modelling of the subsurface geology of Gull Lake, Manitoba
Bibliographic record
Abstract
The purpose of this project is to create a geographic information system (GIS) that will allow researchers to record, analyze, manipulate and model geologic da a. The modelling process being undertaken here has a twofold purpose. The first is to recreate the interpretive capability provided by traditional methods. These traditional methods include the fence diagram, the isopach map and the conventional contour map. The second purpose is to incorporate additional analytical capacity with the traditional cartographic tools that will assist the visualization process as well as providing tools to quantify the observed phenomena. To accomplish these two goals, one must first examine how humans perceive their world and how these perceptions affect the efficacy of any modelling attempt. How humans use a system of logical inquiry to build up a practical and testable model of reality will affect the veracity and effectiveness of any modelling endeavour. The development of a GIS such as this requires the ability to collect, store and manage the data and information used, the ability to conduct robust analysis on these data, the preparation of cartographically sound representations of the analysis, and the capability to integrate the results of any analysis back into the database as the basis for additional study. Interpretation of the representations created will enable the researcher to integrate visually many factors that were previously difficult to assemble in one cogent form.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".