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Record W6999403691

A comparative analysis of conventional and emerging methods for characterizing coastal morphology and change

2016· article· en· W6999403691 on OpenAlexfundaboutno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicScience and Climate Studies
Canadian institutionsnot available
FundersMemorial University of NewfoundlandUniversity of Prince Edward IslandSimon Fraser University
KeywordsFilter (signal processing)Table (database)Work (physics)Event (particle physics)PopulationHazard
DOInot available

Abstract

fetched live from OpenAlex

Small islands and coastal areas are threatened by the negative impacts of climate change. Sea-level rise, increased storm event and frequency, and other coastal hazards are expected to impact infrastructure, settlements, and facilities that support the livelihood of coastal communities. In addition, small islands and coastal communities are often considered to lack the capacity to properly anticipate and adapt to a quickly changing climate. Proper coastal adaptation requires a number of key components including data collection, monitoring and evaluation. This thesis sought to evaluate two methodologies of data collection and monitoring on Prince Edward Island, Canada: one low cost method using terrestrial peg line measurement; and two, the use of low altitude small Unmanned Aerial Vehicles (UAvs) to create high resolution orthomosaics and digital surface models for coastal assessment. Considerations of cost, agility and accuracy of the research methods are made throughout the thesis with an intended application to a long-term monitoring program that can be adopted by other small island and coastal communities around the world interested in improving their resiliency and ability to adapt to climate change.\nAn historical terrestrial measurement method was employed on Prince Edward Island by the Department of Community and Cultural Affairs Marine Environment Section in 1984 but abandoned several years later in the early 1990s. This thesis investigated this method through re-measurement and study of old log books and revealed several inadequacies. Improvements to the historical monitoring method are made through the resurrection and establishment of 74 erosion measuring locations across Prince Edward Island during the 2014 and 2015 field seasons. Measurement of these 74 cliff and bluff coastal environments resulted in an average annual loss of 0.46 m with a single largest loss of 2.69 m. This method is limited by the type of data collection but provides a good starting point for coastal communities with limited knowledge and expertise in the field to begin understanding and quantifying coastal change. Recent developments in Unmanned Aerial Vehicle technology have led to a wide-spread interest in using the technology across many industries and fields of study. A major advantage of using UAVs is their ability to efficiently collect high resolution orthomosaics and elevation models at a fine temporal scale for coastal assessments. This thesis utilized two UAV systems at a study site in North Lake, Prince Edward Island, Canada - a fixed wing system by PrecisionHawk, and a quadcopter by 3DRobotics - and conducted a comparative analysis to determine the best platform for the application to coastal data collection and monitoring. Results found consistently improved performance of the quadcopter versus the fixed wing, including accuracy, a lower upfront cost, and the ability to perform to expectation in high sustained winds. Some results include an image marker to ground control point difference of 0.10 m for the fixed wing and 0.03 m for the quadcopter. The quadcopter showed better results when comparing elevations to a survey grade GPS survey of the study site, and coastal delineations of the orthomosaics showed a slight improvement using the quadcopter. This comparative analysis showed the real possibility of accurately representing a coastal cliff or bluff environment using UAV technology that can be monitored to detect annual change. The ability of UAVs to cost-effectively and accurately produce data rich products leads to the conclusion that the technology provides a realistic alternative to traditional monitoring methods and has great implications for the adoption to monitor coastal environments of small islands and coastal communities.

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)0.017
metaresearch head score (Gemma)0.028
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.034
GPT teacher head0.305
Teacher spread0.271 · 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
Published2016
Admission routes2
Has abstractyes

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