Review of <i>Whooping Crane: Images from the Wild.</i> By Klaus Nigge.
Bibliographic record
Abstract
When asked to review this book I hadn't yet heard of it or its author, a wildlife photographer well known in his native Germany and the author of four photographic books published there, so I was eager to read it. The book is visually impressive, measuring 11" x 12", making it a true coffee-table production. Inside is a well-written 2S-page "primer" on whooping cranes by Krista Schlyer dealing with cranes in myth and legend, crane vocalizations and displays, and breeding biology. She also provides a brief survey of the whooping crane's population history, its near brush with extinction, and the mostly failed efforts since the 1970s to establish additional wild populations. A dozen suggested readings and some relevant websites are also provided. The heart of the book consists of more than ISO spectacular single- and double-page color photographs obtained at the cranes' wintering grounds in and near Aransas National Wildlife Refuge in Texas and at their breeding grounds in Canada's Wood Buffalo National Park. Nigge is the first professional still photographer ever to be allowed to photograph a pair of Wood Buffalo's whooping cranes during the hatching period. This endeavor meant spending six days and nights alone in a cramped photo blind and enduring all the attendant hardships for a once-in-a-lifetime opportunity to document the experience visually.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.059 |
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".