Cross‐jurisdictional collaborative deterioration modeling via hierarchical Bayesian transfer learning
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
Infrastructure performance deterioration models are a critical component in asset management. While many jurisdictions have begun collecting more reliable asset condition data, an effective data-sharing mechanism is still lacking that enables cross-jurisdictional knowledge transfer for developing more reliable deterioration models, particularly for jurisdictions with limited or even no historical data. To bridge this gap, this study proposes a parametric transfer learning framework for collaborative deterioration modeling across jurisdictions by integrating a stochastic process model with a hierarchical Bayesian approach. Transfer learning is realized in two aspects to capture both intra- and inter-jurisdictional heterogeneity: by incrementally updating the learned globally shared information when data from a new jurisdiction become available, and by supporting parameter estimation even when some covariates are partially missing. The proposed framework is quantitatively compared with a jurisdiction-specific modeling strategy in terms of model uncertainty through simulation studies. Furthermore, case studies using a real-world historical bridge condition database collected from nine jurisdictions with different inventory sizes in Canada are conducted to compare independent and collaborative modeling approaches in two aspects: their ability to capture inter-jurisdictional heterogeneity and their impact on model uncertainty on lifecycle decision making. Results confirm the effectiveness and significance of the proposed collaborative modeling approach in infrastructure asset management.
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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 | 0.000 | 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 | 0.001 |
| 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