Bibliographic record
Abstract
G rasslands are not easy to know.Massively transformed in the short span of ten to fi fteen decades, North American grasslands challenge ecologists, who have had little time to understand their "original ecology and function." 1 Contemplated from afar, "they are almost featureless, a horizontal, double-stitched seam between earth and sky.Moving up close is equally challenging.By the time the infi nitely narrow, windblown stems and leaves come into focus, all larger perspective is lost." 2 So mused Don Gayton -a student of western Canadian landscapes whose collection of lyrical essays exploring the transformation of the prairies, titled Th e Wheatgrass Mechanism, won him a reputation as a writer with "the eye of a scientist and the soul of a poet" -as he contemplated Spirit in the Grass, a coff ee-table book on the Cariboo-Chilcotin region of British Columbia.3 Th e photographer's challenges are real.Every "shot" encapsulates a series of decisions: which lens to use; where to direct it; when, in what light, to press the trigger.Every image is composed and framed, as a close-up, a long shot, a view of the middle distance.But are grasslands more diffi cult to encapsulate than other landscapes?Rugged snow-capped mountain peaks, the impressively broad and soaring trunks of old-growth rainforest, rugged shorelines -the classic images associated with western Canada's parks and protected areas, and beloved of tourists and camera buff s -are not typical of these areas.4 But does the photographer out on the range really face a stark choice between uninteresting depictions of a horizontal world and myopic close-ups of ground-hugging vegetation?Although Gayton suggests as much, he praises photographer Chris Harris xii
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.727 | 0.692 |
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".