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Record W1601567170 · doi:10.5772/22533

Heat Transfer for NDE: Landmine Detection

2011· book-chapter· en· W1601567170 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInTech eBooks · 2011
Typebook-chapter
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsHeat transferMechanicsPhysics

Abstract

fetched live from OpenAlex

IntroductionAlthough land mine problems existed in many regions, Bosnia (1995) and Afghanistan (2001) gave the land mine issue a particular sense of urgency.Intended for warfare, these mines remain buried after the end of the conflict.These mines are triggered by civilians causing around 15,000-20,000 victims per year in 90 countries, ICBL (2006).The U.S. State Department estimates that there are around 40-50 million of buried mines that need to be cleared.According to Horowitz (1996) 100,000 mines are found and destroyed per year; thus 450 years will be necessary to clean all mines.However, each year, 1.9 million of new mines are buried.In addition, the presence of mines also causes economic decline being one of the major limitations to agricultural work on these regions, Cameron & Lawson (1998).Thus, it is necessary to develop new techniques which allow to detect mines quickly and with high precision.The Ottawa treaty, Ottawa (1997), banning the production and use of AP mines was signed by 158 countries in 2007 however the most important AP manufacturers, China, Russia, India and EE.UU, have not yet signed it.Nowadays more than 350 types of mines exist, Vines & Thompson (1999); but they can be broadly divided into two main categories:• Antipersonnel (AP) mines.• Antitank (AT) mines.AT mines are relative big and heavy (2-5 Kg) and are usually laid on the ground forming regular patterns and shallowly buried.AT mines have enough explosive to destroy a tank or a truck, as well as to kill people in or around the vehicle; they also require more pressure to be detonated than AP mines.On the contrary AP mines contain less explosive and are lighter than AT mines.AP mines can be buried anywhere, they may lie on the surface or be shallowly buried.Sometimes they are placed in a regular pattern to protect AT mines, however in most cases they are placed randomly.Moreover, as AP mines are light and small, wind or rain can easily move them making their location, even with the original pattern, more difficult.AP mines are designed to damage foot soldiers avoiding their penetration into an specific area.These mines can kill or disable their victims and are activated by pressure, tripwire or remote detonation.These characteristics make AT mine detection and clearance easier than AP mine detection.Detection and clearance of buried mines is a big problem with lots of humanitarian, environmental and economic implications.Current techniques for non-destructive evaluation 2 www.intechopen.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.551
Threshold uncertainty score0.824

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 head0.034
GPT teacher head0.246
Teacher spread0.212 · 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