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Record W2981734474 · doi:10.4095/305898

Habitat characterization of Boltenia ovifera and Modiolus in the Head Harbour/West Isles/Passages ecologically and biologically significant areas, New Brunswick, Canada

2017· report· en· W2981734474 on OpenAlexaboutno aff
C A Mireault, Peter Lawton, Rodolphe Devillers

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsnot available
Fundersnot available
KeywordsHarbourHabitatHead (geology)GeographyFisheryEcologyArchaeologyBiologyPaleontology

Abstract

fetched live from OpenAlex

The Fundy Isles region of the Lower Bay of Fundy in New Brunswick, Canada, is a coastal area with a high benthic biodiversity. This has prompted the designation by the Department of Fisheries and Oceans Canada (DFO) of certain areas of this region as DFO Ecologically and Biologically Significant Areas (EBSA). Boltenia ovifera and Modiolus are two benthic species that have been identified as Vulnerable Marine Ecosystem (VME) indicator species that aid to the uniqueness of benthic habitats, but are vulnerable to disturbance. Those species have thus been considered a key starting point for the assessment of marine species distributions within the EBSA region. Benthic image and video data collected at thirty stations during the summer of 2016 were analyzed for the presence and abundance of B. ovifera and M. modiolus. Target survey strata were derived using depth and slope characteristics from available multibeam data. Near-seabed drift transects were then carried out using a surface-deployed camera system. Twenty-five minute videos were analyzed in real time in lab using Transana 3.0 video analysis software. Images were extracted from the videos using FFMPEG software at 30 second intervals and analyzed using PhotoQuad 2.4. Biological data and a 1m resolution multibeam dataset of the region were used in General Additive Models (GAM) to produce predictive distribution models of B. ovifera and M. modiolus. Preliminary results of these models show that seafloor slope and depth (p = < 0.001, n=809) are variables explaining the distribution of B. ovifera, findings that are consistent with previous studies. However, these models performed poorly in terms of the overall model output for both the image (r2 = 16.71%) and the video analysis (r2 = 9.56%). These new surveys have added significantly to our knowledge of the area- and depth-related distribution of B. ovifera. Finding significant aggregations of this species at depths to 75m on hard substrates suggests that prior assessments on the presence of sensitive benthic habitat in this coastal region underestimated the actual extent. Unfortunately, there were only a limited number of observations for M. modiolus due to difficulties identifying them in the video and image data. This has precluded developing robust GAM models for M. modiolus at this time. The seabed camera was also equipped during the 2016 seabed surveys with a Nikon digital still camera that obtained higher-resolution imagery. Those additional images will be analyzed to help increase M. modiolus observations. Further data analysis will be conducted to refine the GAM species distribution models and test other modelling techniques, such as Maximum Entropy (MaxEnt) and Boosted Regression Tree (BRT). These models will hopefully provide insight on the impacts of the environmental factors that influence the distribution of B. ovifera and M. modiolus within the EBSA region and provide geospatial predictions of high quality habitat for consideration of conservation planning approaches.

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 score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
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 head0.029
GPT teacher head0.227
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 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
Published2017
Admission routes1
Has abstractyes

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