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Record W2008384451 · doi:10.5539/sar.v4n2p70

Observed Effects of Warm Season Perennial Grass Agriculture on Resident Mammal Species

2015· article· en· W2008384451 on OpenAlexvenueno aff
David D. Leimbach, Gregg J. Marcello

Bibliographic record

VenueSustainable Agriculture Research · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsnot available
FundersIllinois Department of Natural Resources
KeywordsPeromyscusPerennial plantBiologyPopulationMammalLolium perenneAgronomyGrowing seasonMonocultureDry seasonEcologyDemography

Abstract

fetched live from OpenAlex

<p>This study focused on the observed impact of switching from a corn / soybean agricultural rotation to the planting of warm season perennial grasses for use in biomass agriculture, as well as the subsequent effects on resident mammal populations. We trapped two parcels of land planted in warm season perennial grasses between September 2011 and October 2013 in order to obtain a census of the mammal populations residing upon the property and to determine the impact (if any) of the change in agriculture. During the first year of the study, a seemingly inhospitable (urban) agricultural area revealed a diverse population of inhabitants. The second year of trapping resulted in the capture of 2 new species (total of 7 species captured) and the noticeable absence of 1 species; a distinct shift in mammal populations had been observed. There was an inverse relationship between the population increase of <em>Peromyscus maniculatus</em> during 2012 and the decline in the population of <em>Peromyscus leucopus. </em>When a Chi-square test was run (<em>P</em><0.05), the hypothesis of competitive exclusion appeared to be supported. In the third year, population numbers of <em>Peromyscus</em> <em>maniculatus</em> had also plummeted. We observed that warm season perennial grass monoculture had an adverse impact on resident mammal species.</p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.067
GPT teacher head0.286
Teacher spread0.219 · 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 teacher head, not a consensus.

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

Citations3
Published2015
Admission routes1
Has abstractyes

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