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Record W4387745901 · doi:10.1016/j.fsisyn.2023.100444

Investigating the availability of 3D-printed firearm designs on the clear web

2023· article· en· W4387745901 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueForensic Science International Synergy · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
Keywords3d printedBlueprintIdentification (biology)Computer scienceThe InternetWeb applicationDownloadWorld Wide WebEngineeringManufacturing engineering

Abstract

fetched live from OpenAlex

The release of the plans of the 3D-printed Liberator firearm sparked a wave of new designs from creators worldwide, resulting in an extensive collection of 3D-printed firearm plans, in particular blueprints, and parts available for almost unrestricted download on the internet. Identifying and categorizing the diverse range of 3D-printed firearms and components pose a challenge due to the abundance of designs available. Between 2021 and April 2023, data was collected on over 2,100 3D-printed firearm plans. While blueprints of fully 3D-printed firearms initially dominated the scene, hybrid designs and parts kit completions / conversions (PKC) have gained popularity for their improved reliability and performance. The now highly networked community offers considerable support with detailed instructions and procedures, providing precise guidance for construction. This systematic classification, grouping and structuration of the recorded data on the Clear Web supported the identification of patterns of the main threat trends related to 3D-printed firearms.

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.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.259
Teacher spread0.218 · 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