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Record W7145127025

大洗研究開発センター廃棄物管理施設における森林火災の及ぼす影響評価

2016· report· ja· W7145127025 on OpenAlexaboutno aff
Yusuke Shimomura, Akira Hanari, Isamu Sato, Ryoichi Kitamura

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2016
Typereport
Languageja
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsFire protectionForest managementResearch centerIntensity (physics)Fire control
DOInot available

Abstract

fetched live from OpenAlex

In response to new standards for regulating waste management facilities, it was carried out impact assessment of forest fires on the waste management facilities existed in Oarai Research and Development Center of Japan Atomic Energy Agency. At first, a fire spread scenario of forest fires was assumed. The intensity of forest fires was evaluated from field surveys, forest fire evaluation models and so on. As models of forest fire intensity evaluation, Rothermel Model and Canadian Forest Fire Behavior Prediction (FBP) System were used. Impact assessment of radiant heat to the facilities was carried out, and temperature change of outer walls for the assumed forest fires was estimated. The outer wall temperature of facilities was estimated around 160$^{\circ}$C at the maximum, it was revealed that it doesn't reach allowable temperature limit. Consequently, it doesn't influence the strength of concrete. In addition, a probability of fire breach was estimated to be about 20\%. This report illustrates an example of evaluation of forest fires for the new regulatory standards through impact assessment of the forest fires on the waste management facilities.

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)0.005
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.367
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.003
Science and technology studies0.0050.007
Scholarly communication0.0020.006
Open science0.0040.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.051

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 head0.041
GPT teacher head0.307
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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 routes1
Has abstractyes

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