Bibliographic record
Abstract
Saddled with common names like skunkbush, stinking sumac, and ill-scented sumac, Rhus trilobata is clearly a shrub in need of a good public relations agent.Those unflattering names refer to the strong scent its foliage and stems emit when crushed.Ignore the unappealing monikers, and you will find that its ornamental and environmental assets are more than sufficient to make R. trilobata a valuable landscape plant.Rhus trilobata has a wide native range in western North America, reaching from the Canadian province of Saskatchewan south to Texas and Mexico but skipping the moist coastal areas of the Pacific Northwest.It grows in many ecological regions, from the Great Plains grasslands to mountain shrubland, chapparal, and forest areas, and is found in association with numerous species of deciduous and evergreen trees and shrubs as well as with grasses and forbs.Within its native range this deciduous shrub can grow from two to twelve feet tall, with four to six feet being typical in most landscape settings; its height is determined in part A lemonade-like drink can be made from the attractive red fruits.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.017 |
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".