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Record W4386375884 · doi:10.1007/978-3-031-34037-6_9

Prairie Grouse

2023· book-chapter· en· W4386375884 on OpenAlexaboutno aff
Lance B. McNew, R. Dwayne Elmore, Christian A. Hagen

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
FundersU.S. Bureau of Land Management
KeywordsGrouseRangelandHabitatGeographyEcologyShrublandVegetation (pathology)Rangeland managementShrubBiology

Abstract

fetched live from OpenAlex

Abstract Prairie grouse, which include greater prairie-chicken (Tympanuchus cupido), lesser prairie-chicken (T. pallidicinctus), and sharp-tailed grouse (T. phasianellus), are species of high conservation concern and have been identified as potential indicator species for various rangeland ecosystems. Greater prairie-chickens are found in scattered populations in isolated tallgrass prairie throughout the Midwest, but primarily occur in the more expansive tallgrass and mixed-grass prairies in the Great Plains. Lesser prairie-chickens occur in mixed-grass, shortgrass, and arid shrublands of the southern Great Plains. Sharp-tailed grouse occur in mixed-grass, shortgrass, shrub steppe, and prairie parkland vegetation types and are broadly distributed across the northern Great Plains, portions of the Great Basin, and boreal parkland areas of Alaska and Canada. Due to reliance on a variety of rangeland types, consideration of management and anthropogenic activities on rangelands are critical for prairie grouse conservation. Grazing is one of the more prominent activities that has the potential to affect prairie grouse by altering plant structure and composition, and recent research has attempted to identify the mechanisms of grazing effects on prairie grouse. Fire is another important disturbance affecting grouse habitat, especially considering how the current distribution and intensity of fire differs from what occurred historically. Additionally, human infrastructure in the form of roads and energy development, as well as land conversion and degradation such as tillage and tree encroachment can fragment and reduce habitat for prairie grouse. Finally, weather including drought, extended rain, and temperature extremes are common across the distribution of prairie grouse. Although not directly under management control, the effects of weather are an overarching factor that need to be considered in conservation planning. This chapter will summarize the life-histories and habitat requirements of prairie grouse, discuss how rangeland management and other human activities affect them, highlight major threats to prairie grouse and provide recommendations for future management and research.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0430.007

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 head0.020
GPT teacher head0.204
Teacher spread0.184 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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