THE BRITISH FLORA IN THE ARCTIC
Bibliographic record
Abstract
I should like to preface a discussion of the flora with a few words about British botanists in the Arctic. It is necessarily few since British botanists, like romantics and artists, gourmets, hedonists and political Europhiles have long been seduced by the lure of the south, Goethes land where the orange trees bloom. In the nineteenth century the only arctic territory in the former British Empire was northern Canada and thanks to largely Admiralty inspired initiatives there was a succession of expeditions seeking to find a vital safe route, the north-west passage, to the Indies. Besides the crew, the expeditions usually included a number of variously qualified scientific members and during the course of these expeditions botanical collections were brought back to Britain to be identified and, not infrequently, written up. Two famous botanists who had a part in this were Robert Brown and William Hooker. Neither were especially interested in the Arctic, or at least not sufficiently motivated to visit it, but both made important contributions in describing species: Hooker, chiefly during the period of his professorship at Glasgow University, prior to his southwards move to Kew. The most important collections processed by Brown were those made by Ross and Sabine on the former’s 1818 expedition (Ross 1819) to Baffin Bay, during which they explored both the Greenland and Canadian shores, and one made by the members of Parry’s
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".