Bibliographic record
Abstract
Drawn since boyhood to the beauty and allure of marshes, naturalist William Burt has prowled them by day and night, in every season, from one edge of North America to the other. For thirty years he has hauled his large-format camera with him, seeking to capture on film the elusive birds, the wildflowers and grasses, and the unique wild beauty of the marshes. In this breathtakingly lovely book, he selects ninety of his most striking photographs. He also offers his reflections on the marshes he has visited, inviting his readers to come with him and become acquainted with this hidden world, its richness, and its vulnerability. Burt explores marshes near and far, from Connecticut to Manitoba, the Gulf of Mexico, California’s Central Valley, the Northern Plains, and elsewhere. His photographs explore all aspects and seasons of marsh life but focus especially on such shy inhabitants as rails, bitterns, grebes, and gallinules. While the photographs tell stories of their own, Burt’s narrative invokes the marshes of the past and compares them to today’s, with prose as picture-sharp as the photography. No book has ever evoked the mystery and beauty of the marshes so compellingly as this by William Burt. And no reader, having accompanied the author to this secret world, will fail to appreciate the rare privilege of having been there.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.170 | 0.043 |
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".