Seeing clearly – Plant anatomy through Katherine Esau's microscopy lens
Bibliographic record
Abstract
Describing, naming and understanding the tissues and cell types composing biological organisms underpin myriad research endeavours in the biosciences. This is obvious when the organismal structure is a direct subject of the investigation such as in analyses of structure-function relationships. However, it also applies when structure represents the context. Gene expression networks and physiological processes cannot be divorced from the spatial and structural framework of the organs in which they operate. Atlases of anatomy and a precise vocabulary are therefore key tools on which modern scientific endeavours in the life sciences are based. One of the seminal authors whose books are familiar to nearly everyone in the plant biology community is Katherine Esau (1898-1997), a phenomenal plant anatomist and microscopist whose textbooks are still used daily around the world - 70 years after their first publication. Several technical innovations in microscopy have emerged since Esau's time and plant biological studies by authors who were trained using her books are shown side-by-side with Esau's drawings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".