Bibliographic record
Abstract
This paper presents laboratory test data on 21 joint seals: 10 field-moulded sealants (FMS; 1- and 2-components polyurethanes, polysulphides, silicones, etc.) and 11 preformed seals (neoprene, silicone, high-density open-cell and low-density closed-cell foams, etc.). The aim was to evaluate their performance in submerged, partially submerged and essentially dry conditions in extremely severe climates. These seals were tested on cement mortar substrates as well as on steel substrates. The tests carried out on FMS were: adhesion-in-peel strength, compression-extension cycling at severe temperatures, Shore A hardness, weatherability and modulus of elasticity, etc. On preformed seals, the tests conducted were weatherability, % recovery and load deformation behaviour, etc. Conclusions, recommendations and the specific suitability of joint seals with cement mortar and steel substrates are reported. The general conclusion is that even though the joint seals evaluated had similar base chemical constituents, they showed variable results. Their properties and characteristics differ from one manufacturer to others, indicating that prior knowledge about their performance is essential to the user. Furthermore, the published data on the performance of seals used in hydraulic structures situated in severe climatic conditions is sparse. It is recommended that utilities publish as much information as possible to help others.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".