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Record W2802195938 · doi:10.7939/r3416t597

Evaluation of Radar and Cameras as Tools for Automating the Monitoring of Waterbirds at Industrial Sites

2014· article· en· W2802195938 on OpenAlexaboutno aff
Sarina Loots

Bibliographic record

VenueUniversity of Alberta Library · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsRemote sensingRadarComputer scienceGeographyTelecommunications

Abstract

fetched live from OpenAlex

Conflict occurs between people and birds at industrial sites around the world, where birds can endanger human lives (e.g. airports) and where bird populations are endangered by human activities (e.g. wind farms). Mitigating these conflicts requires accurate detection of birds and measures of their abundance and distribution. At industrial sites, detection of flying birds and the deployment of deterrents are often automated through detection by avian radar. Such sites include the various oil sands mining operations in northern Alberta, where operators are required to protect migrating waterfowl from landing on potentially toxic waste-water ponds. I tested two technologies for detecting birds in this context, one for flying birds (radar), and one for birds that have landed (cameras). I tested radar to establish its accuracy for detecting flying birds, based on birds detected by paired human observers. I used X-band marine radar and tested two types of radar antennas, one parabolic and one open-array, across a range of conditions at both process-affected water ponds and freshwater ponds. I found that the two antennas failed to detect about half of all detections confirmed by visual observers, both when they were each in operation separately (open-array antenna failed to detect 43% of targets that were confirmed as birds; parabolic antenna failed to detect 56.4% of targets that were confirmed as birds) and when they were in operation together (both antennas operating simultaneously on two radars failed to detect 43% of targets that were confirmed as birds by the visual observers). My results suggest that antenna type, height of radar station, substrate around the station, and site-specific knowledge of target birds should be more explicitly addressed when marine radar is used as part of bird protection programs. A combination of radar types, antennas, and other detection methods may be needed to achieve more comprehensive bird detection strategies at industrial sites. I also tested cameras to monitor birds in the context of industrial ponds. Birds that have landed on ponds are not detectable by radar, and standardised monitoring by human observers has documented tens of thousands of birds landing annually on oil sands process-affected water ponds. Such counts provide information on bird abundance, but there is considerable variation between observers and sites. To overcome these limitations, I evaluated the potential for cameras to monitor birds on industrial water bodies. I compared counts from high-resolution panoramic photos and photos taken by conventional remote cameras to counts conducted by field observers. I also tested the success of a computer algorithm to process photos automatically. High-resolution panoramas recorded two-thirds of bird counts recorded simultaneously by field observers, for distances of approximately 500 m from survey stations. Conventional remote cameras recorded two-thirds of birds in photos clearly, but only to a distance of 100 m. Both single-frame SLR panoramas and single-frame wildlife photos failed to capture birds that dove, birds that were behind other birds, and birds with oblique aspects to the camera. The presence of these birds could be revealed by capturing bird motion with multiple photo frames in short succession (time-interval). Automated processing of time-interval photos produced a very high true negative rate (95%), suggesting that it can substantially reduce the time spent by humans to process photos. The combined application of high resolution photos taken at frequent intervals and a specialized bird detection code makes cameras a viable alternative to human observers. Understanding the distributions and abundance of migratory waterfowl in the oil sands is in the interest of hunters, naturalists and citizens across North America. Radar and cameras can both contribute to this understanding, while simultaneously improving human safety, reducing cost and inter-observer variation, and increasing the duration and frequency of 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)0.005
metaresearch head score (Gemma)0.010
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 score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.000
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 head0.026
GPT teacher head0.210
Teacher spread0.184 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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