The Management And Analysis Of Infrastructure Time Series Data: An Environmental Time Series Database
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
Until recently, the City of Ottawa did not have a centralized and coherent system to manage their long-term water and sewer time series data. Consequently, it was difficult to perform data management tasks, access data, and do useful analysis. The City’s Water Resources Group initiated the Environmental Time Series (ETS) database project. ETS has organizational and time-saving features that reduce human error and make tasks like data loading, validation, and derivation of new data easy to learn and perform. ETS has a simple and powerful means of deriving data that transparently manages data quality. These features facilitate the management of very large amounts of data. A well-organized database system with all required data readily available makes for powerful and flexible data analysis. Its ease of use facilitates detailed as well as broad perception of the City’s infrastructure behaviour. This minimizes assumptions and maximizes optimization of existing and future infrastructure. In short, it promotes good decision-making.
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 | 0.001 | 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 | 0.001 |
| 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