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Record W2039239820 · doi:10.1080/17538940802510273

Geomorphological monitoring of a highly dynamic estuary using oblique aerial photographs

2009· article· en· W2039239820 on OpenAlexaff
Islam Abou El-Magd, P.F. Hillman

Bibliographic record

VenueInternational Journal of Digital Earth · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsBooth University College
Fundersnot available
KeywordsChannel (broadcasting)Remote sensingPierOblique caseEstuaryGeographyAerial photosScale (ratio)Environmental scienceCartographyComputer scienceGeologyTelecommunicationsOceanography

Abstract

fetched live from OpenAlex

Bad weather in many countries limits the use of optical satellite imageries in spatial and temporal monitoring of the environment.In this paper, a series of lowaltitude oblique aerial photos taken on daily, weekly and monthly intervals were used to monitor the geomorphological changes in the upper part of the Mersey Estuary, northwestern England.This low-altitude aerial photo methodology reveals itself to be a satisfying compromise between cost, accuracy and difficulty of implementation.It offered a large amount of information on a spatial and temporal scale aiding in the understanding of channel mobility.This was an important consideration in the sitting and installation of new bridge pier foundations.This series of oblique aerial photos was used in a dynamic model to determine the migration of the ebb channel and was effective in identifying the main route of flow.Few uncertainties were encountered and the level of accuracy achieved in resolving these uncertainties in the images was in the range from 40 cm to a maximum of 1.7 m.This was compared with historical navigation charts and showed good correlation.Further applications are required to improve the quality of the data output from these images and the development of the technique.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.279

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 head0.013
GPT teacher head0.266
Teacher spread0.253 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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