The Global Flows of Iron from Lithosphere to Technosphere
Bibliographic record
Abstract
This record contains the source code, raw and processed datasets, model outputs, and Jupyter notebooks used in the study “The Global Flows of Iron from Lithosphere to Technosphere.” The collection supports full transparency and reproducibility of the analyses presented in the manuscript. The materials document global iron flows from extraction sites to steel production and in-use stocks, providing spatially resolved datasets and computational tools used to generate figures and results. Outputs include NetCDF files, tabular data, and visualizations. Source code reproduces preprocessing routines, modeling steps, and plotting procedures. Jupyter notebooks demonstrate the workflow from data preparation to final results. Contents src/ – Source code for data processing, modeling, and analysis data/raw/ – Raw datasets used in the analysis data/output/ – Cleaned and formatted datasets ready for analysis data/netcdf/ – Final outputs in NetCDF format data/plot/ – Generated plots and figures data/atlas/ – Selected SESAME Human-Earth Atlas (Faisal et al., 2025) datasets used in this study All files are organized so users can recreate the spatiotemporal analysis and adapt the workflow for related research on material cycles or industrial systems.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".