Swedes in Canada In Carl R. Cronmiller’s book, “A History of the Lutheran Church in Canada, ” references are
Bibliographic record
Abstract
sent him to North America to find new varieties of plants and seeds. While in Swedesboro, New Jersey, the pastor of the Swedish Lutheran Church, Rev. John Sandin, died and Peter Kalm was asked to conduct the services. In the summer and autumn of 1749 Kalm visited Montreal and Quebec and received letters of welcome including one from the Governor General of Canada. He collected data on fall wheat, shrubs, trees, and grains, noted his impressions of Montreal, visited the Jesuit College and cloistered convents. The Abbess informed him that she and her sisters would heartily ask God to make him a good Roman Catholic to which he replied that “he was far more anxious to be and remain a good Christian and that as a recompense for their honors and prayers he would not fail earnestly to ask God that they too might remain good Christians.” A second visit to Canada took place in the summer of 1750, travelling by way of Lake Ontario he came to Fort Niagara, and the following day went to Niagara Falls where he wrote a description of the Falls which was given to Benjamin Franklin and published in The Pennsylvania Gazette. Kalm married the widow of Rev. Sandin and returned to Sweden where,in addition to continuing his work in natural science, he became a prominent ordained minister. The accounts of Kalm’s travels have been published both in Swedish and English.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".