Bibliographic record
Abstract
Abstract In the 330s or 320s BC, the Greek explorer Pytheas of Massalia undertook a pioneering voyage to northern Europe. In his report Περὶ τοῦ ὠκεανοῦ (‘On the ocean’), which has come down to us only as a few excerpts quoted by later authors, Pytheas mentions the island Θούλη, which is situated in the far north, six days’ sail from Britain. There is uncertainty as to the location of this island: the Faroe Islands, Iceland, or perhaps somewhere in Norway. Much effort has been spent on explaining Θούλη as a Germanic toponym but none of the proposals is conclusive. It might well be an exonym of Greek origin coined by Pytheas himself. A sound etymology in phonological, morphological and semantic respects can be presented: Θούλη = * t h ū́lē seems to reflect the substantivized feminine form of the adjective PIE * d h uh 2 ló- ‘smoky, steamy, misty, foggy’ (: PIE * d h ṷeh 2 - , * d h uh 2 - ‘produce smoke, steam’) which, following the derivational pattern -o- → -i- , is the base for the noun * d h uh 2 li- f. > OI dhūli-, dhūlī ‘dust, powder’, cp. Lith. dū́lis m., dū́lė f. ‘smoke, mist, fog’ etc. The name Θούλη meaning ‘the misty, foggy one’ would have correspondences in, for example, Fog Islands (British Columbia, Canada) and Eilean a' Cheò ‘island of mist’ (cp. OIr. ceó m./f. ‘mist’), the Gaelic name of the Isle of Skye.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.519 | 0.434 |
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".