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Record W4381140716 · doi:10.3389/fmars.2023.1182395

Investigation of carboxymethyl chitosan in the development of biodegradable soft bait fishing lures

2023· article· en· W4381140716 on OpenAlexafffund
Ryan Legault, Ali Ahmadi

Bibliographic record

VenueFrontiers in Marine Science · 2023
Typearticle
Languageen
FieldEngineering
TopicMarine Biology and Environmental Chemistry
Canadian institutionsÉcole de Technologie SupérieureUniversité de MontréalUniversity of Prince Edward Island
FundersMitacs
KeywordsChitosanUltimate tensile strengthFishingSwellingChemistryFood scienceMaterials sciencePulp and paper industryFisheryComposite materialOrganic chemistryEngineeringBiology

Abstract

fetched live from OpenAlex

This paper describes the development and testing of two compositions of biodegradable soft bait fishing lures. A water-soluble form of chitosan, known as carboxymethyl chitosan, was added to the biodegradable lure formula to investigate if the lure properties, such as tensile strength, swelling, and underwater performance, could be improved. A three-month shelf-life study was completed to compare the lure properties of two compositions of biodegradable baits: the first composition containing no carboxymethyl chitosan and the second composition containing 5 wt.% carboxymethyl chitosan. The baits manufactured with carboxymethyl chitosan showed increased tensile strength and underwater performance compared to the lures manufactured without this ingredient. Also, the lures manufactured with carboxymethyl chitosan showed increased swelling when submerged in fresh water, which is not desired; however, these lures stayed intact longer before beginning to degrade. When submerged in salt water, the lures manufactured with and without carboxymethyl chitosan showed similar characteristics. The following results will assist in completing further formula optimizations to improve other hindering properties of the current biodegradable lures. The development of more environmentally friendly fishing options is needed to preserve the world’s oceans and freshwater systems for the future generation of recreational anglers.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.011
GPT teacher head0.196
Teacher spread0.185 · 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 designBench or experimental
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
Published2023
Admission routes2
Has abstractyes

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