Transferring Cultural Geo-History Datasets of Artifacts Using GIS Geodatabase for Archiving in Dodoma Capital City, Tanzania
Bibliographic record
Abstract
The study aimed to create a historical geographic information system (HGIS), including a geodatabase and simple web application for Dodoma Capital City (DCC) in Tanzania. A web GIS application based is an outcome of the study that can improves research on, and knowledge of, the rare artifacts of cultural and historical heritage in Dodoma Capital City (DCC) for historians and the wider academic community. Likewise, spatial data incorporated allows for visualization of the relationship between people, and their geographic and cultural surroundings. Therefore, the cultural geo-history in this paper describes the specific connection of the cultural artifacts and historical site in a given area to their environment and geographic space. For that purpose, the Dodoma Capital City (DCC) historical artifacts as a case study were cataloged based on GIS techniques, geocoding protocols, and describing the artifacts to create an intuitive and familiar tool for historical researchers and archivists to better understand the cultural geo-history of Dodoma Capital City (DCC). The resulting tool, the Dodoma Capital City Historical Geographical Information System (DCCHGIS), combines a geodatabase and a web application to provide access to a small portion of the geospatial cultural history of Dodoma Capital City (DCC). The DCCHGIS demonstrates that archiving are useful in creating an accurate, informative, and usable Historical Geographic Information System (HGIS) tool that increase the knowledge of and access to cultural geo-history.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".