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Record W2279414732 · doi:10.1111/1556-4029.13025

Forensic Archaeological Recovery of a Large‐Scale Mass Disaster Scene: Lessons Learned from Two Complex Recovery Operations at the World Trade Center Site

2016· article· en· W2279414732 on OpenAlexaff
Scott C. Warnasch

Bibliographic record

VenueJournal of Forensic Sciences · 2016
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsContext (archaeology)ExcavationDebrisDisaster recoveryArchaeologyWorld trade centerTask (project management)Scale (ratio)HistoryForensic engineeringEngineeringGeologyGeographyLawPolitical scienceTerrorismCartography

Abstract

fetched live from OpenAlex

In 2006, unexpected discoveries of buried World Trade Center (WTC) debris and human remains were made at the World Trade Center mass disaster site. New York City's Office of Chief Medical Examiner (OCME) was given the task of systematically searching the site for any remaining victims' remains. The subsequent OCME assessment and archaeological excavation conducted from 2006 until 2013, resulted in the recovery of over 1,900 victims' remains. In addition, this operation demonstrated the essential skills archaeologists can provide in a mass disaster recovery operation. The OCME excavation data illustrates some of the challenges encountered during the original recovery effort of 2001/2002. It suggests that when understood within the larger site recovery context, certain fundamental components of the original recovery effort, such as operational priorities and activities in effect during the original recovery, directly or indirectly resulted in unsearched deposits that contained human remains.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.297
Teacher spread0.259 · 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 designSimulation or modeling
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

Citations14
Published2016
Admission routes1
Has abstractyes

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