Bibliographic record
Abstract
On the scientific name: In 1753, Linnaeus originally named this plant Vaccinium hispidulum (Cafferty & Jarvis 2002). In 1817, Salisbury renamed it Chiogenes serphyllifolig. In the 1950s, scientists saw a connection of the plant to Gaultheria procumbens in terms of its berry and connection to intermediate forms of the genus in South America. Thus, the plant received the name Gaultheria hispidula. Gaultheria comes from a Canadian court physician and naturalist in the early 1700s named Jean-Francois Gaulthier (Sulak 1981). Hispidula is a latin word that refers to the hairs on the leaves and stem (Shackleford 2011). On the common name: G. hispidula and its relative Gaultheria procumbens (Wintergreen) were also called moxie, moxie berry, moxie-plum. G. hispidula was also called creeping pearlberry. The term moxie may have come from the base “mashihka ” (Cree) and “maskikky ” (Ojibwa) meaning “herb infusion ” (Cassidy 1995). Dr. Augustin Thompson named his nerve medicine that could have been made out of G. hispidula or G. procumbens “moxie ” presumably from the Cree and Objibwa medicine (Cassidy 1995, Shackleford 2011). The term moxie was used for a soda drink
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 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.159 | 0.179 |
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".