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Record W4281381945 · doi:10.3389/fmars.2022.869992

Managing Marine Resources Sustainably – The ‘Management Response-Footprint Pyramid’ Covering Policy, Plans and Technical Measures

2022· article· en· W4281381945 on OpenAlexaff
Roland Cormier, Michael Elliott, Ángel Borja

Bibliographic record

VenueFrontiers in Marine Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsFisheries and Oceans Canada
FundersInternational Council for the Exploration of the Sea
KeywordsSustainabilityEnvironmental resource managementEcological footprintFootprintTemporal scalesEnvironmental planningPyramid (geometry)Adaptive managementNatural resourceBusinessCarbon footprintNatural (archaeology)GeographyEnvironmental scienceEcologyGreenhouse gas

Abstract

fetched live from OpenAlex

The plethora of human activities and their pressures and impacts in the oceans require managing at local, national, regional and international scales. This requires management responses in a programme of measures to determine (a) the area in which the human activities take place, (b) the area covered by the pressures generated by the activities on the prevailing habitats and species in which pressures are defined as the mechanisms of change, and (c) the area over which any adverse effects (and even benefits) occur on both the natural and human systems. The spatial and temporal scales of these leads to the concepts of activity-, pressures-, effects- and management responses-footprints, defined here. These footprints cover areas from tens of m 2 to millions of km 2 , and, in the case of management responses, from a large number of local instruments to a few global instruments thereby giving rise to what is termed the management response-footprint pyramids. This may operate from either bottom-up or top-down directions, whether as the result of local societal demands for clean, healthy, productive and diverse seas or by diktat from national, supranational and global bodies such as the United Nations. These concepts are explained and illustrated using marine examples based on experience from many jurisdictions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.061
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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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 teacher head, not a consensus.

Study designObservational
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

Citations40
Published2022
Admission routes1
Has abstractyes

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