Data integration across borders: a case study of the Abbotsford‐Sumas aquifer (British Columbia/Washington State)<sup>1</sup>
Bibliographic record
Abstract
Abstract: Integrating spatial datasets from diverse sources is essential for cross‐border environmental investigations and decision‐making. This is a little investigated topic that has profound implications for the availability and reliability of spatial data. At present, ground‐water hydrostratigraphic models exist for both the Canadian or for the United States (U.S.) portion of the aquifer but few are integrated across the border. In this paper, we describe the challenges of integrating multiple source, large datasets for development of a ground‐water hydrostratigraphic model for the Abbotsford‐Sumas Aquifer. Growing concerns in Canada regarding excessive withdrawal south of the border and in the U.S. regarding nitrate contamination originating north of the border make this particular aquifer one of international interest. While much emphasis in GIScience is on theoretical solutions to data integration, such as current ontology research, this study addresses pragmatic ways of integrating data across borders. Numerous interoperability challenges including the availability of data, metadata, data formats and quality, database structure, semantics, policies, and cooperation are identified as inhibitors of data integration for cross‐border studies. The final section of the paper outlines two possible solutions for standardizing classification schemes for ground‐water models – once data heterogeneity has been addressed.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".