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Record W7133286112

Identification of Representative Seamount Areas in the Offshore Pacific Bioregion, Canada

2022· other· en· W7133286112 on OpenAlexfundaboutno aff
Cherisse Du Preez, Tammy Norgard

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersFisheries and Oceans CanadaNational Oceanic and Atmospheric Administration
KeywordsSeamountHabitatSubmarine pipelineMarine habitatsEcosystemRange (aeronautics)Marine ecosystem
DOInot available

Abstract

fetched live from OpenAlex

The Offshore Pacific Bioregion (OPB) is a dense cluster of ecologically and biologically significant areas, most of which are underwater mountain ranges known as seamounts. Seamounts support a range of ecosystems, depending on a suite of physical and biological characteristics. The Fisheries and Oceans (DFO) Science Branch was asked to develop an ecological assessment to identify representative seamount areas to detect changes within the OPB (i.e., areas that capture examples that reasonably reflect the full range of ecosystems present at the scale of assessment). The focus of the assessment is an Area of Interest (AOI) in anticipation of a proposed Large-Scale Marine Protected Area. Historically little is known about the variety of ecosystems and species supported by the OPB seamounts. Before 2017, research on the OPB seamounts was limited to information from the relatively small fisheries and rare scientific surveys. Since then, the Deep Sea Ecology program (DFO Pacific Region) has led three intensive seamount surveys. Herein we identify and describe representative seamount areas primarily using models, classification systems, habitat level surrogates, and ground-truthing with the new survey data. We also identify new seamounts, new seamount classes, natural seamount boundaries, the ecological uniqueness and ecosystem functions provided by each seamount, species found on seamounts, existing knowledge, and anticipated environmental changes. There are 62 seamounts in the OPB, 47 of which are in the AOI, and dozens that are newly discovered and unnamed. We found that depth- and nutrient-related seamount characteristics are often indicative of enhanced ecological characteristics, where seamounts with shallower summits and higher potential flux of particulate organic carbon support regionally unique or rare species or habitats, higher biomass, higher biological diversity, and more ecosystem functions. Shallower, more productive seamounts are also more likely to have pre-existing data, have attracted previous research, and are more likely to suffer anthropogenic impacts, now and in the future (e.g., fishing and climate change). The evaluation herein determined all seamounts provide rare shallow offshore ecosystems and support ecologically important species (e.g., cold water corals and sponges). However, Union, Dellwood, and Explorer seamounts are unique or rare within the AOI (and the OPB). The establishment of the proposed Marine Protected Areas (MPAs) will significantly enhance the representativity of offshore ecosystems and species within conservation areas. Together with the existing SGaan Kinghlas-Bowie Marine Protected Area, all regional seamount classes will be protected within conservation areas—with only a few examples of notably different seamounts occurring outside of a conservation area (e.g., SAUP 5494 and Tuzo Wilson). SK-B, Union, Dellwood, and Explorer seamounts are also identified as good candidates for representative seamount areas (i.e., reference sites) to detect changes. The ecological assessments within this Research Document are intended to support ongoing adaptive ecosystem management, to be re-examined as questions that arise regarding management and monitoring.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.010
GPT teacher head0.246
Teacher spread0.236 · 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 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

Citations0
Published2022
Admission routes2
Has abstractyes

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Same venueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du CanadaFrench-language works237,207