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Record W7069157027

Safety

2018· article· en· W7069157027 on OpenAlexaboutno aff

Bibliographic record

VenueInsecta mundi · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Agency (philosophy)Variety (cybernetics)MindsetOccupational safety and healthLegislationSafety standardsTask (project management)Control (management)
DOInot available

Abstract

fetched live from OpenAlex

Wildlife damage management (WDM) is an exciting field with many opportunities to provide solutions to the complex issues involved in human-wildlife interactions. In addition, WDM wildlife control operators (WCO) face a variety of threats to their physical well-being. Injuries can result from misused (Figure 1), faulty, or poorly maintained equipment, inexperience, mishandled wildlife, harsh weather, and dangerous situations, such as electrical lines. The goals of this publication are to: * Develop an awareness of safety issues and adopt a mindset of “Safety First”, * Review the major safety threats that WCOs face, * Provide basic information for WCOs to protect themselves, and * List resources for further information and training.\nAlthough no statistics are available for the WDM industry in particular, the authors are aware of several instances where WCOs have lost their lives or suffered serious injuries while performing WDM.\nWhile accidents do happen, most are preventable and occur due to hurried behavior, neglect of procedures, or lack of attention to the task at hand. The United States, Canada, and Mexico have agencies tasked with setting and enforcing standards to assure safe and healthful conditions for workers. The U.S. agency is the Occupational Safety and Health Administration (OSHA). In Canada, it is the Labour Program, and in Mexico, the Ministry of Labor and Social Welfare governs workplace requirements. Readers are encouraged to keep abreast of government safety regulations not only to follow the law, but also to maintain a safe working environment. Safety is an extremely broad and complex topic. The number and diversity of situations that pose safety risks to WCOs are numerous. This publication focuses specifically on safety risks to the WCOs’ physical wellbeing, such as injuries. Safety concerns pertaining to organizational design, worker supervision, disease, environmental or social catastrophes, or pesticides are beyond its scope.\nWork in WDM poses many safety risks to those involved. Awareness, planning, and deliberate action can eliminate or reduce many threats. As the industry continues to develop, WCOs must keep up with new threats and safety practices to maintain their well-being. Following safe work practices helps to ensure WCOs remain on-the-job and injury free.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0340.002

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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes1
Has abstractyes

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