Bibliographic record
Abstract
Understanding surface water and groundwater interactions is crucial to integrated resource management. Obtaining relevant data is a first step, but finding and retrieving the data is challenging because the data are often scattered amongst databases maintained by different groundwater and surface water agencies, such as in Ontario. The emergence of data networks and web portals addresses this challenge partially, insofar as users can then access the data in a uniform way. However, there remain significant challenges in data discoverability and connectivity: (1) web portals are often difficult to find and use, and (2) links between different databases are absent. The latter is a major impediment to integrated resource management, due to the relative unavailability of data about relations between surface water and groundwater entities. Linked Data overcomes these challenges by describing links between entities on the web, using Semantic Web techniques, enabling the relations to be found and retrieved with web browsers and search engines. This complements existing web portals through re-use of their data access mechanisms while providing value-added information in the form of links. Presented will be recent results from a project prototyping Linked Data for the Canadian hydro community. Water wells, aquifers, and monitoring sites from the Groundwater Information Network (Natural Resources Canada) are linked to watersheds, catchments and major water bodies from the National Hydrographic Network (Natural Resources Canada), and to stream gauges from the National Hydrometric Network (Environment and Climate Change Canada). This enables users to find and retrieve targeted information about surface water and groundwater interactions in the region. Planned future work includes expanding nationally within Canada, as well as internationally to the US to capture cross-border interactions. These early results position Linked Data as the next frontier in providing data for integrated resource management, particularly in situations where data is distributed amongst many agencies, as is the case in Ontario.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.030 | 0.031 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".