Bibliographic record
Abstract
In the summer of 1998, award-winning writer and biologist Bernd Heinrich found himself the unwitting -- but doting -- foster parent of an adorable gosling named Peep. Good-natured, spirited Peep drew Heinrich into her world -- one he found to be filled with as much color and drama as that of her human counterparts. And so, with a scientist's training and a nature lover's boundless curiosity and enthusiasm, Heinrich set out to observe and understand the travails and triumphs of the Canada geese, or honkers, living in the beaver bog adjacent to his rural Vermont home. His presence in the bog, at all hours, in all weather, became as commonplace as that of the local beavers and birds. The resident geese learned that Heinrich could be trusted, enabling him to watch and record their daily routines from up close. Heated battles over territory, mysterious nest raids, jealousy over a lover's inattention, all are recounted here in an engaging, anecdotal narrative that sheds light on how geese live and why they behave as they do. Far from staid or predictable, the lives of geese are packed with adventure and full of surprises. In The Geese of Beaver Bog, Heinrich takes his readers through mud, icy waters, and overgrown sedge hummocks into a seemingly impenetrable world. He does so with deft insight, respectful modesty, and infectious good humor. Illustrated throughout with Heinrich's trademark sketches and featuring beautiful four-color photographs, The Geese of Beaver Bog is part love story, part science experiment, and wholly delightful.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".