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Record W7066280987

Francis Drake's Course in the North Pacific, 1579

2025· article· en· W7066280987 on OpenAlexaboutno aff

Bibliographic record

VenueDigitalCommons - Kennesaw State University (Kennesaw State University) · 2025
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsNoticeImpartialityAlbatrossRidiculousCrewCourse (navigation)Astronomer
DOInot available

Abstract

fetched live from OpenAlex

The year 1979 is to be a magical one. We will be reUving the excitement of the passage of a tiny ship called both the Pelican and the Golden Hind (also speJled Golden Hinde) and her dashing commander, Captain Francis Drake, and his crew as they visited the Pacific Coast of North America exactly tour hundred years ago this summer. We are celebrating a cuadricentennial. For over twenty years now it has been the privitege of the writer as a serious historical scholar to seek to iron out the details of the Drake voyage in our part of the globe. Such research is really detective work. At a moment's notice Kenneth Holmes, the teacher of college students, has become Sherlock Holmes, a kind of historical and geographical detective. It has been necessary to search for clues in such varied places as the Pacific Northwest, California, western Canada, and England. The sleuthing has called not only for a search in historical documents and ancient maps, but for the acquiring of knowledge of winds and weather, of ocean currents, of bays and headlands, and of the ways of windjammers in the distant past. Then, having gathered clues of all kinds in many places, there comes the time to bring them together and to seek out the answers to a number of searching questions that have bedeviled every swish of the sails. The entire project demands discipline and impartiality . as much as is possible in a human endeavor. The answers are often gnawingly tentative· hardly ever as certain as one would wish. Yet it is the task of the historian to ask the right questions and to come up with the most plausible answers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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
Published2025
Admission routes1
Has abstractyes

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