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Record W7164157907 · doi:10.4050/f-0079-2023-1298

Analysis of Aerial Firefighting with Rotorcraft Platforms

2023· article· W7164157907 on OpenAlex
Shawn Melhorn, Monica Gil, Jordan Gorelick

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicFire Detection and Safety Systems
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsFirefightingHuman lifeWildfire suppressionFire protectionWater supplyAerial survey

Abstract

fetched live from OpenAlex

Wildfires are an annual torment in many parts of the world. They can spread very quickly and destroy forests, wildlife, buildings, infrastructure, and homes, as well as risk human life before they are able to be controlled. Because of the rapid response time that is necessary to extinguish a wildfire before it becomes too large, the quantity and speed of water delivery are extremely important - helicopters are a great tool for accomplishing this goal. They can fly directly to the fire and refill their water supply from smaller, more remote water sources than other aerial platform options require. There are several water tank options to transport and drop water on the fire from a helicopter. This paper focuses on four of these options: an external rigid tank, an accordion tank, a Bambi Bucket® 1, and an internal tank in addition to the outlining some of the options for remote water sourcing. Each of these tank types have various advantages and disadvantages which are discussed using models developed for evaluating helicopter firefighting applications.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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