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

Design Considerations to Optimize Monitoring for Pacific Region Fisheries

2019· article· en· W6958475516 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsFishingCredibilityFisheries scienceFisheries managementMarine fisheriesArchipelago

Abstract

fetched live from 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 Unpublished report prepared for the Pacific Region Monitoring and Compliance Panel, and Fisheries and Oceans Canada (DFO) by Archipelago Marine Research, Ltd., Victoria,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.

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)0.030
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.030
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.068
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.004

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 head0.109
GPT teacher head0.277
Teacher spread0.168 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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