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Record W4384697670 · doi:10.25071/2561-5467.1074

Boye Meyer-Friese and Albrecht Sauer, Johan Månssons Seebuch der Ostsee von 1644 by Jacob Bart Hak

2023· article· de· W4384697670 on OpenAlexvenueno aff
Jacob Bart Hak

Bibliographic record

VenueThe Northern Mariner / Le marin du nord · 2023
Typearticle
Languagede
FieldArts and Humanities
TopicHistorical and Archaeological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPhilosophyTheology

Abstract

fetched live from OpenAlex

Knowledge of their surroundings has always been important for seafarers, be it the weather, the state of the sea, sailing tracks or destinations.In fact, even without help, most sailors are able to predict the weather for the next twentyfour hours.Information on sea lanes, ports, depths and prevailing winds, for example, are less variable and more valuable to compile for future voyages.Once Sweden had taken control of the Baltic in 1658, it was considered an inner sea, from Atlantic Norway to Finland's Bothnian Bay.Sweden decided that the existing knowledge of the coast, rivers, and ports, till then only known by pilots and fishermen, was to be gathered and made available for the Admiralty and its officers.Naval officer Johan Månsson had been working for the Swedish Admiralty in Stockholm, rising through the lower ranks from 1632 until in 1643, he was ordered to collect information on the shipping routes and coasts of the Balticum, and to share his information on ports, landmarks, river mouths, water depths, etc.He was given command of the pinnace Phoenix and in August 1643, he set sail for present-day Germany and the south coast of Sweden.In 1644, in Stockholm, Månsson published his Seebuch, with all the hallmarks of a nautical almanac.The collected information was not only for merchant shipping purposes, but the Swedish Navy also greatly benefitted from Månsson's work.Between 1664 and 1786, the Ostsee Seebuch was published in 23 issues in four languages; Swedish, German, Russian and Danish.The foreign-language books and charts of the Baltic at that time were not flawless, however, since the use of different languages could easily lead to errors in the nautical publications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0390.031

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.

Opus teacher head0.020
GPT teacher head0.207
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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