Steel Manufacturers at the Margin in Nova Scotia
Bibliographic record
Abstract
fnwood At the end of the 1890s the Canadian economy began to expand and diversify at an unprecdented rate [$4, p. 55]. Prominent among the changes was an enormous expansion of iron and steel output [19]. The largest single contribution to Canada's iron and steel "takeoff " came from a 1,000-ton-per-day plant at Sydney, Nova Scotia. This plant relied hea•41y u•on ore from a mine located on Bell Island, Newfoundland. Because of its mec•ocre quality, the ore lay idle until technological change expanded the range of workable resources at the very end of the nineteenth century. Although technical progress brought steel manufacture with Newfoundland ore inside the margin of profitability, the Sydney steel plant continued to be handicapped by the poor quality of its resource base. Iron was known to be present on Bell Island at least as early as the 1570s, during which decade ore samples were sent to England [29]. During the early 1600s detailed plans were made to mine and smelt the
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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; both teacher heads agree on what is shown here.
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".