Database for In-field Condition Assessments of Flood Control Infrastructure and Prioritization of Remedial Action Budgeting
Bibliographic record
Abstract
Since the passage of the Conservation Authorities Act in 1946, Authorities have been involved in assessing flood risk and implementing programs and projects related to providing flood protection to areas of existing development within their watersheds.Following Hurricane Hazel, in 1954, the Authorities adopted flood control as a core function and began to study and implement flood protection works in earnest.The amalgamation of four existing Conservation Authorities into the Metropolitan Toronto and Region Conservation Authority (TRCA) in 1957 allowed for a more integrated approach to studying, prioritizing and funding such works across the region, as reflected in the TRCA 1959 Plan for Flood Control.Subsequent to the development of this plan, a multi-phased approach to providing flood protection began, including both structural and non-structural approaches to providing protection.It is the structural approaches that this study will deal specifically with, and include dams, weirs, drop structures, channels, and dykes.Many of these structures reflect the engineering approaches of the day, with the use of hardened surfaces such as concrete channels designed to convey flood waters in the most hydrologically efficient manner.Many of the original works constructed in
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.037 |
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".