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Record W7066503553

Identification of key chemical components responsible for attracting American lobster (Homarus americanus) to Atlantic herring (Clupea harengus) bait

2020· article· en· W7066503553 on OpenAlexaboutno aff

Bibliographic record

VenueRepository@Hull (Worktribe) (University of Hull) · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersUniversity of Hull
KeywordsHerringForage fishAtlantic herringKey (lock)ForageFish <Actinopterygii>Identification (biology)Competition (biology)
DOInot available

Abstract

fetched live from OpenAlex

Atlantic herring (Clupea harengus) is the principal bait used to capture American lobsters (Homarus americanus) in North America. The American lobster fishery is amongst the most lucrative fisheries in both the USA and Canada. However, there are emerging economic and environmental concerns regarding the long-term viability of the Atlantic herring supply chain. The depletion of the forage fish stock, subsequent frozen storage and transport for it to be deployed as bait in the American lobster fishery results in a negative environmental impact. Additionally, growing competition from other sectors for a limited supply of forage fish has led to the bait supply becoming increasingly costly and uncertain.An alternative bait that utilises off-the-shelf attractants to lure American lobsters into traps would remedy the emerging concerns regarding the use of forage fish as bait. To facilitate the development of a synthetic bait, this study sought to identify the key chemical components responsible for attracting American lobsters to the traditionally used bait, Atlantic herring. Firstly, a bioassay was developed to observe whether a given substance is capable of attracting lobsters towards an odour source and retaining their interest. This assay was utilised in various ways to obtain a greater understanding of the attractive properties of Atlantic herring throughout this thesis.Attractive components in herring were characterised as stable to decomposition, as herring samples were aged for up to 14 days and found to retain their attractive properties, both in the laboratory and in the field. The natural extract was then purified using a bioassay-guided purification approach. Based on the information gathered in the characterisation and purification, amino acids were deemed the primary candidate class of compounds. The key amino acids responsible for distinguishing between attractive and less attractive tissues were identified by combining chemical profiling procedures with chemometric analysis techniques. The resulting understanding then advised the formulation of a synthetic baiting solution. This solution comprised of eight amino acids in the natural ratios observed in the profile of herring muscle tissue. This synthetic solution was then trialled under laboratory conditions and found to attract a greater number of subjects than whole Atlantic herring in flowing water conditions. The synthetic solution developed in the present study is the first step towards developing a more sustainable bait to replace the environmentally damaging and economically unstable practice of using forage fish as bait.

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.019
GPT teacher head0.234
Teacher spread0.215 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRepository@Hull (Worktribe) (University of Hull)Same topicMagnetic confinement fusion researchFrench-language works237,207