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Record W6987853521

Unveiling the legacy of the nineteenth century Riotinto mining railway: from historic heritage to thriving tourist attraction

2024· article· en· W6987853521 on OpenAlexfundno aff

Bibliographic record

VenueUPM Digital Archive (Technical University of Madrid) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanism, Landscape, and Tourism Studies
Canadian institutionsnot available
FundersRio Tinto
KeywordsTourismThrivingPort (circuit theory)Heritage tourismIndustrial heritageTourist attractionRailway lineCultural heritagePower (physics)
DOInot available

Abstract

fetched live from OpenAlex

The Riotinto mining railway is a remarkable construction. Stretching an impressive 348 km it was built between 1873 and 1875 to transport minerals from Riotinto's mining operations to the international port of Huelva. At its height in the 1950's, this monumental railway network had a fleet of 162 locomotives (mostly steam but also diesel and electric) and around 3,300 freight cars and carriages. Towards the end of the 1960's the line began to fall into disuse, and it was closed entirely in 1984. Since the establishment of the Rio Tinto Foundation in 1987, dedicated efforts have been made to preserve this invaluable railway heritage and today, the fruits of their labor can be enjoyed at the Riotinto Mining Park where tourists can ride a fully restored 22 km section of this historic rail network. The park is highly successful and has recovered strongly after the COVID-19 pandemic attracting a record 96,935 visitors in 2022. The majority of the park's tourists are from Spain but also a significant number are international (principally from Germany) highlighting the global importance of this site and the railway as a sustainable heritage tourism destination. Taking the restoration of the Riotinto mining railway as a case study, we aim to demonstrate the transformative power of the preservation and restoration of industrial heritage.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.215
Teacher spread0.205 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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