Investigation of carboxymethyl chitosan in the development of biodegradable soft bait fishing lures
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".