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Record W3044253134 · doi:10.32873/unl.dc.zea.1103

The Lives, Lore, and Literature of Cranes: A Catechism for Crane Lovers

2020· article· en· W3044253134 on OpenAlexaboutno aff
Paul A. Johnsgard, Tom Mangelsen

Bibliographic record

VenueZea Books · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeFolkloreMythologyHistoryWildernessGeographyFisheryEcologyArchaeologyClassicsBiology

Abstract

fetched live from OpenAlex

This book provides basic information on cranes that should be of interest and importance to crane-loving birders (“craniacs”) as well as to ornithologists and wildlife managers. Primary consideration is given to the sandhill and whooping cranes, but all 13 of the Old World cranes are also discussed. Special consideration is given to the relative abundance and conservation status of all of the world’s species, of which nearly half are declining and a few are in real danger of long-term survival. More than 80 refuges and preserves in the United States and Canada, where the best chances of seeing cranes in the wild exist, are described, as are several zoos and bird parks with notable crane collections. Descriptions of 16 North American annual crane festivals and information on more than 50 birdfinding guides from regions, states, and provinces where cranes are most likely to be seen are included. Lastly, there is a sampling of American, European, and Oriental crane folklore, legends, and myths. The text contains more than 50,000 words and nearly 350 literature references. There are more than 40 drawings and 3 maps by the author and 19 color photographs by Thomas D. Mangelsen.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.012
Scholarly communication0.0070.007
Open science0.0010.003
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.010
GPT teacher head0.209
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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