ADVANCES IN GEOSPATIAL STATISTICAL MODELLING, ANALYSIS AND DATA MINING
Bibliographic record
Abstract
Large volumes of geospatial data are increasingly being collected, stored and disseminated under both commercial and open data practices.The combination of traditional methods in statistics and exploratory data analysis together with the novel principles in data mining and knowledge discovery has provided an expanded analytical toolbox for geospatial data analysts.Recent advances in geospatial modelling and analysis have now enabled the use of high analytical and processing power to deal with massive data collections.In this Geomatica Special Issue, the goal is to share original and innova tive research contributions from geospatial statistics, including relevant methods from the domains of geospatial modelling, analysis and data mining.This issue originates as a follow-up to the Joint International Conference on Geospatial Theory, Processing, Modelling and Applications held on October 6-8, 2014, in Toronto, Canada.An open call together with an invitation to authors of selected papers that were presented at the Conference was circulated in December 2014, seeking submissions related to topics including, but not limited to, the following: dx.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".