Bibliographic record
Abstract
Peter Kalm (1716–79) was a Finnish-Swedish botanist who travelled extensively to observe the natural world in Sweden, Finland, Russia and Ukraine and became a professor of 'oeconomie', - the economic application of subjects such as mineralogy, botany, zoology and chemistry - at the university of Turku. Between 1747 and 1751 he set out on a journey through eastern North America to gather specimens, especially from regions with a similar climate to Sweden. Because Kalm travelled though the area when much of it was still unknown to Europeans, this work has some of the first recorded accounts of places such as Niagara Falls. Kalm played an important part in forging scientific links between Sweden, England and North America. This three-volume work details his travels, and was first published in English in 1770–1. Volume 3 focuses on Kalm's observations of plants and animals in Canada, especially around the French-speaking settlements.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".