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Record W2408309527

Student Independent Projects Environmental Studies 2015: Moose Vehicle Collisions: Solutions for Reducing the Number of Accidents on Newfoundland’s Highways

2015· article· en· W2408309527 on OpenAlexaboutno aff
Alex B. Wright

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsUrbanizationWildlifePovertyEnvironmental planningNatural resource economicsBusinessPollutionProduct (mathematics)Environmental protectionGeographyEconomic growthEconomicsEcology
DOInot available

Abstract

fetched live from OpenAlex

The world today is becoming increasingly urbanized; this is the cause of many new challenges that previous generations have not had to face. Some of the most obvious and crucial problems are air and water pollution. As cities grow, the need for more cars and related industrial activities increases leading to an increase in air pollution, the same can be said for water pollution as areas become increasingly urbanized it becomes more difficult to manage waste leading to dangerous runoff into rivers and streams. Along with these troubling environmental problems, urbanization can also lead to a many social problems such as increased poverty and crime as well as the development of slums in underprivileged neighborhoods. With the persistence of these huge environmental and social problems it is easy to see how one major by-product of urbanization may get overlooked, and that is the increasing amount of interaction between humans and wildlife.

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)0.001
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 score0.652
Threshold uncertainty score0.693

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1010.010

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 head0.076
GPT teacher head0.321
Teacher spread0.244 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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