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Record W2105946907 · doi:10.1144/1467-787302-040

Statistical evaluation of anomaly recognition performance

2003· article· en· W2105946907 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.

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

Bibliographic record

VenueGeochemistry Exploration Environment Analysis · 2003
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsAcadia University
Fundersnot available
KeywordsAnomaly (physics)Anomaly detectionComputer sciencePattern recognition (psychology)Artificial intelligencePhysics

Abstract

fetched live from OpenAlex

Over the past several years there have been a number of ‘new’ selective extraction/partial digestion (SE/PD) methods introduced to the mineral exploration industry. Some of these are truly novel, employing emerging technologies and recent chemical discoveries to digest specific mineral components of geochemical samples. Others represent improved, but recycled, historic approaches that benefit from advanced instrumentation and knowledge to surpass the performance of historic SE/PD techniques. Nowadays, most major commercial geochemical laboratories offer their own versions of a variety of SE/PD approaches, and all claim thattheir versions offer significant exploration advantage over conventional analytical techniques. However, a significant number of geochemists remain unconvinced regarding the advantage that some of these SE/PD techniques offer. This is due to a large number of factors, including: i) the lack of disclosure of the geochemical procedures involved in the digestions, ii) the lack of knowledge of what is actually being extracted from a sample by these methods, iii) the lack of an adequate number of objective assessments of these techniques in orientation surveys, and iv) the lack of adequate rigorous comparisons of the results of these new techniques with those from conventional (trusted) exploration methods. The objective of empirical assessment of a new exploration technique is to determine whether the new technique provides exploration advantage over competing, established methods. Exploration performance can be determined using the hypergeometric probability of obtaining a result by chance that is equivalent in performance to the results of an orientation survey testing a new SE/PD method. The lower the hypergeometric probability of a result equivalent to that from an orientation survey, the more likely the exploration method successfully detected the presence of mineralization. This probability is thus a quantitative measure of exploration performance that allows rigorous comparison of conventional andnew exploration techniques. Furthermore, this statistical procedure for assessing exploration performance of new SE/PD techniques provides the objectivity required to evaluate the effectiveness of any new exploration method.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.637
Threshold uncertainty score0.999

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 studies0.0000.000
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 head0.041
GPT teacher head0.233
Teacher spread0.192 · 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