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Record W1516469135

Cougars and the community

2008· article· en· W1516469135 on OpenAlexvenueno aff
Amy E. Ryken, Laura Bowers Foreman, Margaret Tudor, Gary M. Koehler

Bibliographic record

VenueSound Ideas (University of Puget Sound) · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLivestock and Poultry Management
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental educationMathematics educationGeographyPedagogySociologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

In a research collaboration with government biologists and university educators, K-12 students in the Cle Elum-Roslyn (CER) School District in eastern Washington are investigating where cougars ("Puma concolor") go when their habitat gives way to new housing developments. Now in its seventh year, Project Cougars and Teaching (CAT) is taking the education and science partnership a step further by incorporating civics into the environmental education curriculum. Through this model, students become civically engaged by conducting field investigations of the indigenous cougar's ecology and making public presentations to the community. This article describes the project's use of two curriculum models--one for field investigations and one for civic participation--in the context of studying human/cougar interactions. These models can also be used to guide other community studies. In addition, the curriculum is a prime example of how a community wildlife problem is bringing together diverse community interests to address a given need. (Contains 3 figures.)

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0240.015
Scholarly communication0.0090.004
Open science0.0010.012
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0360.001

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 head0.026
GPT teacher head0.179
Teacher spread0.153 · 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 designObservational
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

Citations1
Published2008
Admission routes1
Has abstractyes

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