Towards a provenance-aware spatial-temporal architectural framework for massive data integration and analysis
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
Spatial-temporal computing refers to the modeling, management, and analysis of spatial and temporal information. Despite the recent advances in massive data manipulation, software system approaches that support the massive spatial-temporal data integration and analysis still face numerous challenges, including the lack of: (i) a high-level architectural framework for massive data integration and analysis; (ii) explicit integration and analysis abstractions; (iii) representations of integration and analysis resources; (iv) explicit provenance representation; (v) reusability of integration and analysis steps; (vi) reproducibility of studies; and (vii) models to build and customize integration and analysis applications. This paper proposes the design and implementation of a high-level domain-specific architecture for data integration and analysis that supports building applications in the spatial-temporal domain. The proposed approach describes three types of first-class citizens, which include abstractions to represent data sources, analysis models, and integration operations. It also benefits from domain-specific languages (DSLs) for high-level representations. To make provenance explicit, the proposed approach identifies three types of provenance information, namely description, analysis, and execution, which help to address reusability and reproducibility. Finally, this approach also supports a model-driven technique to generate integration and analysis steps.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it