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Record W1534449818

Icon Under Fire: The Giant Canada Geese of Rochester, Minnesota

2012· dissertation· en· W1534449818 on OpenAlexaboutno aff
Daniel Eckberg

Bibliographic record

VenueUniversity of Minnesota Digital Conservancy (University of Minnesota) · 2012
Typedissertation
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsIconGeographyGenealogyHistoryCartographyArchaeologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

For over 30 years the giant Canada goose was thought to be extinct, but in 1962 the species was rediscovered in Rochester, Minnesota. Ever since, the city has held a special bond with the species and specifically its local flock. As the goose population has grown, it has become, in some eyes, an intolerable nuisance and a public health threat. In response to complaints, local officials have taken steps to limit human contact and stymie the flock’s growth, polarizing people supporting and opposing the measures and leaving the city at a crossroads. By analyzing the diverse actions and outcomes of communities across North America that have faced similar issues with Canada geese and acknowledging the unique cultural and economic ties between Rochester and its geese, a course of action is developed and recommended. Such a plan would balance the well being and contentment of the city’s residents with the local connections to the flock, and thus necessarily retain at least some of the geese, while improving their management.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.017
GPT teacher head0.224
Teacher spread0.207 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations2
Published2012
Admission routes1
Has abstractyes

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