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Record W2049286703 · doi:10.7901/2169-3358-2001-2-923

RESPONDING TO THE BIG SPILL OF 20111

2001· article· en· W2049286703 on OpenAlexaff
Bill Lehr, Ron Goodman

Bibliographic record

VenueInternational Oil Spill Conference Proceedings · 2001
Typearticle
Languageen
FieldComputer Science
TopicDigital and Cyber Forensics
Canadian institutionsImperial Oil (Canada)
Fundersnot available
KeywordsDisseminationOil spillThe InternetComputer scienceEnvironmental scienceEnvironmental resource managementTelecommunicationsWorld Wide WebEnvironmental engineering

Abstract

fetched live from OpenAlex

ABSTRACT The authors use a hypothetical spill incident 10 years in the future to examine the possible advances of spill response technology. The status of remote sensing at present, as well as its capabilities a decade hence, are discussed. The authors examine spill communication improvements, speculate on the use of the Internet to disseminate spill information, and examine electronic database systems for slick management. Progress in effectively using alternative cleanup strategies such as in situ burning and dispersants are reviewed, along with some of the likely impediments to their use in spills of 2011. Spill trajectory and behavior forecasting techniques of tomorrow are discussed in light of the expected continuing advance in computer technology. The authors review the likelihood that these new capabilities would actually be implemented. The resulting picture is a mixed one. Possible positive and negative scenarios are described.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.030
GPT teacher head0.254
Teacher spread0.224 · 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 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

Citations1
Published2001
Admission routes1
Has abstractyes

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