Bibliographic record
Abstract
In most gravity fed water transmission pipelines, it is desired to reduce excess pressure head to prevent undue strain on a pipeline and lower the incidence of leaks. Normally this is done with pressure reducing valves that essentially burn off this excess pressure as heat. The LucidPipe™ system converts it to low cost electricity thereby removing unwanted pressure and generating energy at the same time - energy that can be put used behind the grid or put back on the grid. The LucidPipe™ system extracts a small percentage of pressure head providing nearly invisible operation allowing water operators to fulfill their primary mission of delivering water to consumers. Because the LucidPipe™ system extracts only a small percentage of pressure head when operating, and about 1 psi of pressure head when stopped, it is virtually invisible in a pipe network and can be placed directly in-line without the need of a bypass for the turbine. Traditional hydropower turbines effectively halt the flow of water when stopped, and can cause dangerous water hammer when grid power is disconnected so they must be placed in a bypass to not interrupt the safe delivery of water. Also, traditional hydropower turbines operate in a narrow band of pressures and flows whereas the LucidPipe™ system has a wide operating range typically found in municipal water transmission system. The LucidPipe™ system adjusts to meet the demands of water delivery rather than a water operator having to adjust the water to meet the demands of the turbine.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.109 | 0.080 |
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".