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Enregistrement W6958475516 · doi:10.6084/m9.figshare.7631588

Design Considerations to Optimize Monitoring for Pacific Region Fisheries

2019· article· en· W6958475516 sur OpenAlexaboutno aff

Notice bibliographique

RevueFigshare · 2019
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueMarine and fisheries research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFishingCredibilityFisheries scienceFisheries managementMarine fisheriesArchipelago

Résumé

récupéré en direct d'OpenAlex

McElderry, H., Meintzer, P. 2019. Design Considerations to Optimize Monitoring for Pacific Region Fisheries. Unpublished report prepared for the Pacific Region Monitoring and Compliance Panel, and Fisheries and Oceans Canada (DFO) by Archipelago Marine Research, Ltd., Victoria, BC. 40p. Comment on a draft national fishery monitoring policy and guidance on implementing the national fishery monitoring policy: This paper provides an overview of Pacific Region fisheries and their underlying catch reporting tools, with the aim of examining integrated approaches for fishery monitoring and identify design considerations to assist managers when considering ways to address monitoring system weaknesses. This purpose aligns with Step 4 – Specifying Monitoring Requirements of the National Fishery Monitoring Policy Implementation Guidelines (Fisheries and Oceans Canada, 2018a).Canada’s Pacific Region fisheries contain a diverse array of capture species, harvest groups and fishing methods; and the information systems that are built around these fisheries are equally diverse in structure and content.Catch reporting has been defined in terms of an assortment of methodologies, called ‘catch reporting tools’ that provide data to an integrated information system, called the ‘catch monitoring system’. At a basic level, the tools are separated into two categories; those that acquire data directly from the fishery participants, termed ‘self-reporting’ tools, and those that involve dedicated investments to independently gather data from the fishery participants, termed ‘independent reporting’ tools.The information needs of all fisheries contain data elements that may limit or conflict with the self-interests of the individual fishery participants. Therefore, data collected from self-reporting tools will always be subject to credibility challenges, whether justified or not. Other factors such as the complexity of the data itself (e.g. species identification), and inconsistent methodologies (method and timeline for measurement) may also create data quality challenges with self-reported tools.Independent reporting tools typically provide more credible and higher quality data, but often at a substantially higher cost than self-reporting tools. Because these tools are essentially investments that layer over the operational activities of a fishery, the cost for these tools varies widely from fishery to fishery due to different fishery characteristics and other external factors. As well, there are different service delivery options for these programs that influence cost and efficacy.Within a fishery there may be multiple tools used to capture the full scope of information required. The integration of these catch reporting tools is a critical design process for a fishery. Several design considerations are presented in this document including the impact of fishery characteristics, efficacy of catch tools, cost factors, compliance issues, and coverage levels. Cost is a significant limiting factor for the application of many independent reporting tools. This is particularly the case because of the lack of proportionality between the level of investment and the desired results. For example, a 50% increase in the monitoring investment may only result in a marginal improvement in the quality of fishery data.The strength of fishery monitoring systems should be routinely evaluated, and improvements considered. In our view, improvements to fishery monitoring systems lie with new tools, improvements to existing tools, increased integration of existing tools, and measures to strengthen compliance, particularly in fisheries with a high self-reporting component. It is notable that that most Pacific Region fisheries are already using the catch reporting tools most suited to their specific fishery characteristics and information needs and examples of where new tools could be applied appear limited. Several avenues for improvements were identified, including reduced timelines, increased coverage levels (e.g., independent monitoring), greater emphasis on participant engagement and the use of technology. Recognizing that self-reported data are a component for most fisheries, and the only option for some, the level of catch reporting compliance is a key issue that potentially undermines the value these information systems. In most instances, compliance with self-reporting requirements is impossible to measure or verify directly.In terms of the National Policy and Implementation Steps, it appears that there remains a gap in ‘policy to practice’ relating to decisions for determining the specific monitoring measures most appropriate for each fishery. The high integration dependency among multiple tools and multiple design elements within specific tools points to a need for a more systematic approach to monitoring program design. While it is recognized that monitoring needs should be evaluated on a fishery by fishery basis, target and bycatch species in Pacific Region fisheries span multiple fisheries. This stove-piped fishery by fishery monitoring design may result in unintended consequences for participant trust and confidence when certain species have poor monitoring in one fishery and good in another. Given the inevitability of self-reporting continuing for many of the catch reporting systems, continuous effort is needed to ensure that trust and confidence in fishery information systems is achieved and maintained.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,030
score de la tête « metaresearch » (Gemma)0,068
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,030
Score d'incertitude au seuil0,157

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0300,068
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,001
Communication savante0,0030,005
Science ouverte0,0030,003
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0120,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,109
Tête enseignante GPT0,277
Écart entre enseignants0,168 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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