Bibliographic record
Abstract
The use of Geographic Infor- mation Systems (GIS) in archaeology seems like a perfect match of technology and application. GIS has found its way into many areas of archaeological research, especially in the area of Cultural Resource Manage- ment (CRM). While GIS offers many tools for the archaeologist, its full potential has not been realized. This paper offers a con- ceptual framework in which GIS procedures can be detailed, as well as a description of those procedures. The state of archaeologi- cal GIS in Canada is reviewed, with emphasis on both the academic and CRM applications of GIS. Finally, the paper examines the pos- sibilities of archaeological GIS. R e s u m e . Lu t i l i s a t i o n d e s s y s t e m e s dinformation geographiques (SIG) en archeologie represente le mariage parfait de la technologie et de son application. Les SIG sont presentement integres dans plusieurs domaines de recherche en archeo- logie, surtout dans le domaine de la gestion des ressources culturelles. Bien que les SIG offrent plusieurs outils de recherche pour les archeologues, leur potentiel n'a pas encore ete exploite. Cet article pro- pose un cadre conceptuel dans lequel les procedures SIG sont decrites. Une mise a jour de lutilisation des SIG en archeologie au Canada est faite, en mettant l'accent sur les applications academiques ainsi que dans la gestion des ressources culturelles. Finalement, le potentiel futur des SIG en archeologie est explore.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".