Bibliographic record
Abstract
In the early 1930s a rich deposit of fluorspar, a mineral used in the production of aluminum, steel, non-stick coatings, and fire retardant clothing, was discovered on the southern tip of Newfoundland. Two mines were established and by mid-century were major employers in St Lawrence. In the 1950s physician Cyril Walsh noticed a marked increase in cases of cancer in the miners. By the late 1960s nearly twenty percent of St Lawrence households had lost a family member to lung cancer. John Martin tells the history of Newfoundland's fluorspar mines from their founding to the last shipment of fluorspar in 1990 and declaration of bankruptcy a year later. He focuses on the health hazards experienced by the miners, and how the mining companies, workers, governments, and health services came to terms with the unfolding human tragedy. He also covers such matters as the improvement of methods for dust quantification and radiation surveillance in the mines, battles for compensation, and the influence of the St Lawrence case on the development of labour law in the province. Martin's compelling history takes on new significance as the mines are set to reopen with the support of the provincial government and renewed confidence in the community due to modern ventilation technology and vigilant monitoring.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".