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Record W3042853456 · doi:10.21001/itma.2020.14.10

Some Problematic Animals in Marco Polo’s Description of the World

2020· report· en· W3042853456 on OpenAlexaff
Stephen G. Haw

Bibliographic record

VenueImago temporis medium Aevum · 2020
Typereport
Languageen
FieldArts and Humanities
TopicLinguistics and language evolution
Canadian institutionsSt. Stephen's University
Fundersnot available
KeywordsGermanTigerHistoryGeographyCartographyGenealogyZoologyBiologyMathematicsArchaeologyAlgorithm

Abstract

fetched live from OpenAlex

There are a number of animals mentioned in Marco Polo’s book which present difficulties. A few are considered here. The papiones of the area near Fuzhou have been identified as Chinese ferret-badgers. It is suggested that this is probably correct. Wehr’s hypothesis that the word “rondes” is a transcription of the Turkic and Persian term qunduz is discussed and rejected: “rondes” must be considered a scribal error. Marco’s “lions” are usually tigers, but not always. Medieval European conceptions of the tiger are examined. Marco’s references to the lynx are also discussed. The term ercolin has never been convincingly explained. It is shown that it almost certainly means “squirrel” and is probably derived from a German word.

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)0.001
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.007
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.058
GPT teacher head0.274
Teacher spread0.216 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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