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Record W2997805611 · doi:10.1002/rse2.142

Spatial and temporal persistence of nearshore kelp beds on the west coast of British Columbia, Canada using satellite remote sensing

2019· article· en· W2997805611 on OpenAlexafffundabout
Sarah Schroeder, Leanna Boyer, Francis Juanes, Maycira Costa

Bibliographic record

VenueRemote Sensing in Ecology and Conservation · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsWildlife Conservation Society CanadaUniversity of Victoria
FundersPacific Salmon FoundationDigitalGlobe FoundationUniversity of Victoria
KeywordsKelpKelp forestHabitatEcosystemEcologyEnvironmental scienceAbundance (ecology)Substrate (aquarium)Spatial ecologyOceanographyTemperate climateGeographyFisheryBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Bull kelp Nereocystis luetkeana is an important foundation species, providing structural habitat and nutrients to the nearshore ecosystems of temperate coastal regions in the Northeast Pacific. Sensitive to environmental conditions, this species thrives in cool, nutrient‐rich water. Reported declines in the extent and distribution of bull kelp may reflect changing oceanic conditions and result in breakdown of important food chains and ecosystem services. This study uses satellite remote sensing to map kelp bed extent from 2004 to 2017 in the Salish Sea on the West Coast of British Columbia, Canada and examines the relationship between trends in kelp persistence with local and global scale environmental conditions. In our study area, we found limited evidence of kelp decline. Local scale effects of current speed, temperature and substrate type may play a role in the spatial and temporal patterns of persistence. Kelp persistence was higher in sites with rocky substrate and lower in areas with low current and gravel or sand substrate. A decline in kelp was recorded from a high in 2015 to a low in 2017; however, a longer and more complete record is needed to distinguish declining trends from natural variability. This work highlights the importance of continued collection of long‐term data for use in time series of kelp abundance as multiple factors can influence the reliability of image interpretation and kelp classification.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.331
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.182
Teacher spread0.167 · 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.

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

Citations30
Published2019
Admission routes3
Has abstractyes

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