Data Quality of OpenStreetMap for Industrial Sites in the Arctic
Bibliographic record
Abstract
Abstract. Climate change is causing rapid warming in the Arctic region, resulting in the thawing of permafrost. This has substantial environmental implications, such as the release and mobilisation of contaminants from past and present industrial activities. However, freely accessible public geographical information is scarce on industrial sites and activities in much of the Arctic, which makes scientific research such as impact assessment difficult. OpenStreetMap (OSM) can be a valuable resource for identifying and assessing industrial sites for contamination. However, OSM data quality is not uniform across regions necessitating our evaluation of its reliability for identifying industrial sites and contamination hotspots in the areas most susceptible to permafrost thawing. Therefore, we examined in our study the object and attribute completeness as well as the currentness of OSM data on industrial sites. Our study focused on the regions defined by the presence of either discontinuous or continuous permafrost located in Canada, the USA, Denmark, Russia, and Norway, as these regions are expected to show strongest impacts of rising temperatures with respect to industrial pollution. The highest object completeness and currentness were obtained in Denmark (99% and 48% respectively). Russia had the lowest completeness (68%) and Canada had the lowest currentness (30%). Despite the promising average completeness of 86% and the average currentness of 35%, only 5.6% of industrial sites mapped in OSM contained information on the type of industry. This finding highlights the need for efforts to enhance attribute completeness gaps to maximize the use of OSM data in comprehensive environmental analyses.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
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".