Nineteenth century maritime weather data from historical New England whaling ship voyages (1820-1890)
Bibliographic record
Abstract
Maritime weather data contained in ship logbooks are used to assess historical changes in global surface wind and precipitation patterns since the early 19th century. We focus on unexploited caches of archival documentation, namely U.S. whaling logbooks of voyages spanning the period 1820 to 1890 from New England archives housed by the New Bedford Whaling Museum, Nantucket Historical Association, and Providence Public Library. The logbooks, often covering multi-year voyages around the globe, contain systematic weather observations (e.g., wind strength/direction, sea state, precipitation, cloudiness) at (sub-)daily temporal resolution. The qualitative, descriptive wind and precipitation recordings by the whalers are quantified and compared with reanalysis products where applicable.Following extensive quality control, we find overall good agreement in wind strength and direction for the whaling logbook wind records with reanalysis products for mean and seasonal climatologies around the world. Interannual variations in North Atlantic wind fields associated with the North Atlantic Oscillation or changes in characteristics of the Azores High subtropical pressure system are also captured by the whaling ship recordings. Predominant precipitation patterns around the world oceans can be captured and variations across a range of timescales are assessed.Our results demonstrate that the historical records provide an important long-term context for changing maritime wind and rainfall patterns in remote ocean regions lacking observational records during the 19th century. Challenges and opportunities for data rescue and digitization of maritime weather records in these under-utilized historical ship logbooks for climate purposes will be discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".