Bibliographic record
Abstract
［1］Nyerges T L. Cognitive aspects of human-computer interaction for geographic information systems. Kluwer Academic Publishers, 1995. 287～310 ［2］Churcher N, Churcher C. GroupARC——a collaborative approach to GIS. In: Proceedings of the 8th Colloquium of the Spatial Information Research Centre. University of Otago, 1996. 156～163 ［3］Obermeyer N J, Pinto J K. The role of geographic information within an organization' s MIS. In: Managing GISs. The Guilford Press, 1994.35～51 ［4］Spencer B. Spatial data warehousing. In: Proceedings of GIS AM/FM ASLA' 97 & Geoinformatics' 97 on Mapping the Future of Asia-Pacific. China: Taipei. 1997.545～553 ［5］Chen J, Jiang J, Yan R,et al. Integrating and managing heterogeneous geographic information for urban planning & land administration. Acta Geodaetica et Cartographica Sinica, 1998,27(2): 153 ～ 160 ［6］Wu R. Towards an enterprise-wide GIS system: a case study. In: Proceedings of GIS AM/FM ASIA ' 97 & Geoinformatics'97 on Mapping the Future of Asia-Pa-cific. China:Taipei, 1997. 657～671 ［7］Chen J,Jiang J,Jin S, et al. designing a collaborative work system by integrating GIS with OA. Journal of Remote Sensing, 1998,2 (3): 59～64 ［8］Goodman J N. Alberta land related information system-a federated database case study. In: Proceedings of URISA, 1994 ［9］Igras E. A framework for query processing in a federated database system: a case study. In: Proceedings of URISA, 1994 ［10］Sheth A P. Federated database systems for mapping distributed, heterogeneous, and autonomous databases.ACM Computing Surveys, 1990,22(3): 183～235 ［11］Newton P W,Gipps P G,Crawford J R. Virtual planning and design: an emeging paradigm. In: Proceedings of 4th international conference on computers in urban planning and urban management. Australia: Melbourne, 1995.27～38 ［12］Bennett W S. Visualizing software —a graphical notation for analysis, design, and discussion. New York: Marcel Dekker, Inc. , 1992 ［13］Teisseire M, Poncelet P, Cicchetti
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".