Bibliographic record
Abstract
With its seemingly endless array of colourful forms and structures, the plant world has inspired generations of artists and illustrators, resulting in a spectacular wealth of paintings and illustrations that have served to inform and captivate its many audiences.Approaches to working from plants refl ect the diversity of source material and the intention of the artist, from the anatomical accuracy for purposes of identifi cation to expressive interpretation.The development of digital imaging within the arts and sciences over the past twenty years has been swift and impressive and its affect on the forms of creation has been marked and unavoidable.We have become as accustomed to viewing images of outer space developed from data sent back from the Hubble Telescope or live views from within the human body.However, in a climate where programmes are constantly being developed to facilitate the production of visual spectacle, the ability to retain the trace of the artist's hand becomes more diffi cult.For the past ten years, the author has worked with botanists at the Royal Botanic Gardens, Kew, exploring the creative potential of plant material at a microscopic level.While working with a variety of microscopic processes and imaging technologies, issues have arisen concerning the status of the fi nal image.The evolution of the work during this period has sought to address some of these issues.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".