Unveiling the legacy of the nineteenth century Riotinto mining railway: from historic heritage to thriving tourist attraction
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".