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 asset management domain has an extensive advancement in condition assessment and rehabilitation decision models. However, most of the focus is devoted to pipelines, giving little attention toward manholes. Recent studies revealed that more than three-million manholes in the United States (U.S.) have structural deficiencies. Defective manholes are a main source of the inflow/infiltration and contribute up to 50% of the collection system’s input to treatment plants. As a result, it is of great importance to assess them on a regular-basis to avoid any operational and structural failure. The main objective of this study is to develop a condition assessment model for sewer manholes. The model divides the manhole into several components and filters the possible observed distress in each element. Later, the study determines the relative importance weight of each component using the analytic network process (ANP) decision-making method. Moreover, the condition of the manhole is computed by aggregating the condition of each component with its corresponding weight. As a result, the proposed assessment model will enable decision-makers to have a final index suggesting the overall condition of the manhole and a backward analysis to check the condition of each component. Thus, better decisions are made pertinent to maintenance, rehabilitation, and replacement actions.
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 | 0.000 | 0.000 |
| 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