Data management and quality control
Bibliographic record
Abstract
Abstract It is often said that data gathering is much more expensive than data-management software (such as geographic information system). Indeed, data are perhaps the most important element of any asset management approach. In this comprehensive chapter, we embark on a journey through digital era, highlighting the pivotal role of data in our contemporary world, emphasizing the importance of data in today's landscape. We delve into the critical question of which data to collect, providing insights into the strategic selection of data based on a cost–benefit approach and the significance of anticipation in data collection. A three-layer approach, encompassing object, system, and urban fabric levels, is proposed as a structure to organize data, elucidating the diverse information requirements at each layer, from descriptive data to performance assessments and requirements. A substantial portion of this chapter is devoted to data models and bias, elucidating the complexities of modeling sewer pipe deterioration and addressing issues such as selective survival and recruitment bias. Quality control emerges as a pivotal concern, clarifying the requirements for data quality, methods to assess completeness, and handling issues such as incompleteness, timeliness, uncertainty, and imprecision. Questions related to data quantity are explored, discussing the data-loop problem, reconstruction methods, and the implications of big data. Practical considerations related to data access and storage are also addressed. The chapter concludes by three enlightening case studies illustrating real-world applications of data models.
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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 | 0.030 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".