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A Fifteen Year Geological Journey with the IUCN Natural World Heritage Sites: Geoscience Education and the Conservation of Nature

2024· preprint· en· W4401865952 on OpenAlexaboutno aff
Mike Katz

Bibliographic record

VenuePreprints.org · 2024
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeotourism and Geoheritage Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsIUCN Red ListNatural heritageNatural (archaeology)World heritageNature ConservationEarth scienceGeographyGeologyArchaeologyEcologyTourism

Abstract

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As an International Union for Conservation of Nature (IUCN) voluntary reviewer of the natural World Heritage sites nominations focusing on criteria (vii) superlative natural phenomena and (viii) geological history I had the opportunity of evaluating 13 nominations from 12 countries from 2009 - 2022 from the Central Highlands of Sri Lanka to the Vatnajokull National Park Iceland. An early appreciation of the natural history and landscapes were developed as a geologist studying, working and visiting various areas in the USA and Canada. Further experience, as an academic in Sri Lanka and Australia, where I also had the opportunity of various assignments and meetings that afforded field trips and study tours to many World Heritage sites in Asia, Africa, South America and Europe. In this paper, I highlight the following sites that were subject to IUCN World Heritage evaluations and their potential for geo-education: Central Highlands Sri Lanka, China Danxia, Ningaloo Australia, Trang An Vietnam, Lut Desert Iran, Barberton South Africa, Trondek Klondike Canada, Vatnajokull Iceland, Classic Karst Slovenia, Sof Omar Ethiopia, Ha Long Bay – Cat Ba Archipelago, Vietnam and Evaporitic Karst Caves of Northern Apennines, Italy. From these experiences it is apparent that this relevant topic, the conservation of nature, should be an important part of the geoscience’s study curriculum along with other social and environmental subjects.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.703

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 science0.0010.000
Research integrity0.0000.002
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 head0.043
GPT teacher head0.280
Teacher spread0.237 · 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 designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

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