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Record W7037439834

The Effects of Conventional Oil Wells and Associated Infrastructure on the Abundances of Five Grassland Songbird Species in Alberta’s Mixed-grass Prairie

2016· dissertation· en· W7037439834 on OpenAlexfundaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2016
Typedissertation
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
FundersCenovus Energy
KeywordsNucleofectionGestational periodDiafiltrationHyporeflexiaFusible alloyProteogenomics
DOInot available

Abstract

fetched live from OpenAlex

Although grassland bird populations have steadily declined, little research has examined the effect of oil infrastructure on abundances of grassland songbirds. Even less research has identified mechanisms that explain observed effects. To evaluate this, I sampled abundance of 5 songbird species at oil well sites with different pump mechanisms, power sources, and activity levels; I also evaluated the effects of perch and road density and exotic vegetation, all of which are associated with oil development. Both Baird’s sparrows (Ammodramus bairdii) and Sprague’s pipits (Anthus spragueii) had lower abundances at all sites that contained oil infrastructure. The other 3 species, chestnut-collared longspurs (Calcarius ornatus), western meadowlarks (Sturnella neglecta), and Savannah sparrows (Passerculus sandwichensis), were relatively unaffected by oil wells, linear features, or exotic vegetation. Given that oil well sites negatively affected two species of concern, more research is needed to determine mitigation strategies.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.817

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.179
Teacher spread0.174 · 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
Published2016
Admission routes2
Has abstractyes

Explore more

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