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Record W2981498825 · doi:10.4095/296973

A preliminary volcanic ash fall susceptibility map of Canada

2015· report· en· W2981498825 on OpenAlexaffabout
M C Kelman

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsVolcanic ashVolcanoGeologyArchaeologyPhysical geographyGeographyGeochemistry

Abstract

fetched live from OpenAlex

Volcanic ash has the potential to impact the economy and human health in many ways, including interfering with air travel, damaging machinery, damaging crops, and contaminating water supplies. We present a semi-quantitative 1:12 million scale map of volcanic ash fall susceptibility in Canada (Figure 1), which shows the locations, types, and ages of 320 Canadian volcanoes with the potential for future eruptions, and four zones of estimated annual ash fall probability. Hazards other than volcanic ash are assumed to affect areas smaller than the symbols used to depict the volcanoes; therefore, although areas susceptible to all other volcanic hazards are encompassed by this map, the map only explicitly delineates areas susceptible to volcanic ash hazards. Four elliptical volcanic Ash Fall Hazard Zones (A, B, C, and D) are defined around Canadian and American volcanoes that pose high ash hazards for Canada, and then merged to form regional ash hazard zones. For the two outermost zones, C and D, the shape, size, orientation, and placement of ash fall hazard ellipses are based on ash distribution data compiled for the six largest ash fall events in Canada during the last 10,000 years. Zone D is defined for hypothetical eruptions of Volcanic Explosivity Index (VEI) ?6 (mp;lt;"hugemp;gt;"), while Zone C is defined for hypothetical eruptions of VEI 5-6 (mp;lt;"largemp;gt;"). Volcanoes are placed at the leftmost foci of the ellipses, which are oriented eastwest for Canadian and Alaskan volcanoes, and northeast for volcanoes in Washington and Oregon. The distance from each volcano to the far margin of the hazard ellipse is estimated based on average maximum dispersal distances (Dx) for the compiled Holocene ash data. Annual ash fall probabilities for zones C and D are based on the number of huge and large eruptions that deposited significant ash in Canada during the last 10,000 years. Since there were 2 huge events, the annual probability of ash fall at some unspecified point within zone D is 1 in 5000. Since there were 2 huge events and 4 large events, the annual probability of ash fall at some unspecified point within zone C is 6 divided by 10,000, which is approximately 1 in 1700. For Ash Hazard Zone B (mp;lt;"mediummp;gt;" events, VEI 4-5), we assume that the apparent frequency-magnitude trend for Pleistocene and Holocene eruptions of VEI?5 within or on the border of Canada (1 huge event, 2 large events) is meaningful. By this reasoning, we would expect 4 medium eruptions. Therefore, we estimate 4 medium ash fall events within Canada during the last 10,000 years. For Ash Hazard Zone B, we would thus predict a total of 10 ash fall events (including the 6 events used to define Ash Hazard Zones C and D), leading to an ash fall recurrence interval of 1000 years, or an annual probability (of ash fall at some unspecified point within zone B) of 1 in 1000. The average maximum ash dispersal distance for Zone B is crudely estimated based on ash fall data from the 1980 Mount St. Helens eruption. Because actual data about Canadian eruptions smaller than VEI 5 are scarce, we have not estimated the number of Holocene events for Ash Hazard Zone A (mp;lt;"smallmp;gt;" events, VEI<4); we simply assume that the annual probability of ash fall (at some unspecified point within zone A) is higher than for Zone B (therefore, >1 in 1000). The maximum dispersal distance, Dx, for Zone A, is speculatively extrapolated from Zone B.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.018
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.008
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.002

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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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 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

Citations0
Published2015
Admission routes2
Has abstractyes

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