Bibliographic record
Abstract
These datasets contain spreadsheet weather data collated from Hudson’s Bay Company ships' logbooks between 1750 and 1850. Each dataset contains relevant accompanying metadata. These logbooks are valuable for covering the whole of the 100-year study period of the ARCdoc project (1750 to 1850) https://arcdoc.wordpress.com/ with only a few gaps (four years only are missing). The original Hudson's Bay Company ships' logbooks are held in the Manitoba State Archives (http://www.gov.mb.ca/chc/archives/hbca/) but microfilm copies are available in the UK National Archives in Kew (http://www.nationalarchives.gov.uk), the latter are however not of good quality. The original logbooks were prepared by the various captains who sailed annually between London and the Company’s factories in Hudson’s Bay. The ships set out in early summer, returning usually in September; voyages planned to avoid the winter ice. They tended to take the same route each year following more-or-less the same latitude, and for the purposes of climate studies this is a great advantage as replicate routes provide for a geographically consistent data set. The Hudson’s Bay Company logbooks are a unique and complete set of merchant shipping documents that have provided much useful Arctic climatic information not available from any other source. Related publications: Ward, C. and Wheeler, D. (2011) Hudson’s Bay Company ship’s logbooks: a source of far North Atlantic weather data. Polar Record doi:10.1017 Ayre, M., Nicholls, J., Ward, C. and Wheeler, D. (2015), Ships’ logbooks from the Arctic in the pre-instrumental period. Geoscience Data Journal. doi: 10.1002/gdj3.27
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".