Bibliographic record
Abstract
Prince of Wales’ Fort near Churchill, Manitoba, Canada, has forty 18 th century cast iron cannon set on its ramparts and exposed to the elements. Two hundred and fifty years of corrosion has removed historical detail. Although there is minimal pollution, (the neighbouring ocean has a low saline content and is frozen for much of the year), relative humidity is very high and windborne particles continually expose new material to corrosion processes. With the failure of numerous protective coating regimes, a fundamental re-examination of the problem of “preserving the cannons’ fabric for future understanding, appreciation, and study” was initiated in 1996. Since no corrosion data for grey cast iron in a sub-arctic, marine environment was available to provide a scientific basis for resource management decisions, a thirty year corrosion rate study using coupons of like alloy has been instituted. Two questions have been posed in the study: what is the corrosion rate; and how long will it take at that rate to obliterate remaining surface detail? This paper presents the corrosion rate results for the first five years of the study. Also offered for discussion will be the question: what is an “acceptable” rate of material loss for cultural resources in an uncontrolled environment where access is difficult and visitation low?
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".