Wading Deep into Canal Spatial Data: The "Geo" in RDM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For the very first time… The 19th century Historic Welland Canals in Canada have been digitally reconstructed overlaying the current landscape and published as an interactive visualization web mapping tool for the world to explore, The Historic Welland Canals Mapping Project. Data generated from this HGIS research project includes layers upon layers of geospatial data, created using GIS technologies, that intricately define canal features. The routes, towpaths, locks, weirs, weirponds, and all surviving features are nicely netted in a local geodatabase, with nowhere to share. So, what’s next? Understandably, the “D” in RDM is an all-inclusive term for many data formats. However, geospatial data introduces a level of complexity where RDM tools can fall short in achieving their goal. As part of the scholarly process, this study explores a “researcher’s” approach to [geo]data management. In 2015 The Canadian Association of Research Libraries launched the Portage Network, with the aim to provide support, expertise, and tools needed to manage research data. An assessment of the Portage tools as they apply to geospatial data – the DMP Assistant (data management plan); RDM Primer; metadata creation; and data services – will be the focus of this talk. A demonstration of the canal data re-used to locate a sunken canal schooner - the subject of an upcoming archaeology dig – will be presented, as well as stories of creating canal geodata.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.313 | 0.003 |
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 it