Bibliographic record
Abstract
One aspect of the generally acknowledged historical influence of German on Slovene is extensive lexical borrowing. The natural world represents a semantic field in which substantial borrowing occurred, and previous studies have demonstrated that analysis of plant names of foreign origin can shed light on the otherwise obscured meanings of these names in Slovene. One sub-field of the natural world that has received relatively little linguistic attention is mycology; specifically, the names of individual species of fungus. Because of the popularity of mushrooms in the cuisine and folklore of all Slavic nations, investigations in this area have particular cultural significance. A multi-language comparison of designations for various fungus in Slavic and geographically adjacent languages makes it possible to identify which names are likely the result of loan translation and which names are likely the result of chance similarity due to salient features of the fungus. This analysis identifies not only a robust number of Slovene names that are likely of foreign origin, but also sets of names limited to a sprachbund of German and German-adjacent Slavic languages. In addition to uniquely German-Slovene pairs of fungal names, this article also identifies German-Slovene/croatian and German-Slovene/croatian-West Slavic correspondences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".