Bibliographic record
Abstract
Abstract Thermoplastic-Lined, Fiberglass Reinforced Plastic (Dual-Laminate) tanks and vessels are typically employed in service conditions that are beyond the limits of even the high performance resins, used in FRP fabrication. Therefore, their design and manufacture requires a great deal of expertise in both the processing and fabrication of the thermoplastic liner, as well as in the design of the FRP structural laminate. A significant investment in tooling and manufacturing equipment is also necessary, to ensure that a high level of quality is achieved in the thermoplastic liner fabrication. Thermoplastic-Lined FRP equipment is generally produced under the controlled conditions of the manufacturer’s plant, which limits the dimensions of the equipment to whatever can be transported to its final destination. When a project in the Far East called for twenty-eight vessels and towers, some of which were close to thirty feet in diameter, Fabricated Plastics Limited, of Maple (Toronto), Ontario Canada, was awarded the contract and faced several critical challenges in order to satisfy the requirement. This Paper outlines the manufacturing procedures employed to fabricate the Dual-Laminate Tanks and Processing Vessels, as well as detailing the challenges that were faced throughout the manufacturing and shipping of the equipment and how these were addressed.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".