Data management and quality control
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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