US Marine Meteorological Journals
Bibliographic record
Abstract
this series consists of volumes entitled meteorological journal a regulation navy issue publication which were to be completed by masters of merchant vessels during their voyages period of record 1878 1894 peak 1885 1886 continuing the pioneering work undertaken by lt matthew fontaine maury united states navy the u s navy s hydrographic office endeavored to enlist the help of commercial vessels in compiling data regarding meteorological conditions at sea these meteorological journals were completed by the captains of over 650 merchant vessels transiting the atlantic indian and pacific oceans although the majority of the vessels represented in the journals were american vessels from great britain germany austria france canada italy and other nations are included each journal documents the passages of one vessel usually for a two month period of time entries were made daily noting particulars of the weather during the day also noted are the course and speed of the vessel measured at two hour intervals along with the distance traveled the tif images created from microfilm by contractors with noaa s climate database modernization program cdmp original bound volumes reside in record group 27 at the national archives at college park md including 31 journals that were inadvertently misfiled at nara among the us maury collection and which have not yet been microfilmed or digitized it is estimated these journals would add 28 000 observations from 1879 1890
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.102 | 0.146 |
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".