Bibliographic record
Abstract
A new thermal analysis technique was developed and tested. It makes use of the improvements in heat transfer characteristics associated with recent advances in heat pipe technology. Heat is extracted from a liquid sample of a melt taken in-situ from within a vessel or furnace. The rate of heat extraction is such as to cause the sample to solidify. The technique was tested both in the laboratory and on an industrial scale (Grenville Castings, Perth, Ontario). Aluminum alloys including 356, 319, Al-xSi, Al-Si-Cu-xMg, and 6063 were subjected to various melt treatments and were used to carry out the tests. Classical thermal analysis was also carried out simultaneously under the same melt conditions using a preheated graphite cup. The comparison showed that the new technique has great potential over classical thermal analysis. The major advantages of the new method are that it conducts the analysis inside the melt (since it is no longer necessary for a physical sample to be removed from the melt itself), it consumes less time and the cooling rate can be precisely controlled during the solidification process. Moreover, it produces curves of greater detail and of better resolution than conventional techniques. In fact, the detail is of such resolution that, in some cases, the cooling curves may be used to infer the chemical composition of certain components of the melt, a fact which equates to a form of rapid chemical analysis. The peaks in the signal which refer to intermetallic formation are of better resolution and more identifiable when the new technique is used. The size of the peaks obtained using the new probe is about three times greater than that obtained by the classical method. With this new technique it becomes possible to correlate the area below the intermetallic peak to the concentration of iron or copper in the melt. This is a feature which makes the new thermal analysis probe act as a rapid chemical analyzer for selected constituents.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".