Bibliographic record
Abstract
Flores P. Murgatroyd I hate the flower of wood or common field. I cannot love the primrose nor regret The death of any shrinking violet, Nor even the cultured garden's banal yield. The silver lips of lilies virginal, The full deep bosom of the enchanted rose Please less than flowers glass-hid from frosts and snows For whom an alien heat makes festival. I love those flowers reared by man's careful art, Of heady scents and colours: strong of heart Or weak that die beneath the touch or knife, Some rich as sin and some as virtue pale, And some as subtly infamous and frail As she whose love still eats my soul and life. Theodore Wratislaw Hothouse Flowers (from Orchids, 1896) flaventes odi flores odique nigrantes quos vilis campus vile nemusque creant. cumque humiles violae, culti cum flosculus horti vulgaris marcens deficit, haud doleo. miror virgineis argentea lilia labris et gremio magicas multa tumente rosas. sed miror flores mage quos sub frigore servat aestibus externis exhilarans hyalus. flores namque adamo quos ars alit anxia, flores qui feriunt nasum percutiuntque oculos, et firmo qui corde valent, floresque caducos, quos tactusve manus ferrea falxve necat. hi sunt virtutis purae pallore decori, hi sceleri similes luxuriare solent, hi furtim infames, infirmi more puellae, nunc quoque quae nostram rodit amata animam. [End Page 221] P. Murgatroyd McMaster University Copyright © 2011 Mouseion
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.684 | 0.436 |
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".