En litteraturstudie om grundvattendata och modeller som möjliga verktyg inom mineralprospektering
Bibliographic record
Abstract
In Sweden, geological mapping, geophysics, and drilling are primarily used as exploration methods, but research and international case studies show that groundwater is an untapped source of information that can make the search for ore both more accurate and more sustainable. As mineralisations weather, metals such as copper, zinc, or lithium leach into the groundwater and are transported with the flow. By combining chemical analyses of these traces with 3D models such as MODFLOW and AI algorithms, it becomes possible to visualise where hidden ore bodies are likely located while simultaneously predicting how mining activities could affect groundwater dynamics and quality. Countries like Australia, Canada, and China have used this approach to reduce the number of uncertain drill holes, discover new deposits, and identify environmental risks earlier in the permitting process. Sweden maintains a nationwide groundwater monitoring network, but the station density is low in its key ore provinces and concentrations of metals relevant to exploration are rarely measured, making the current data insufficient for exploration purposes. To make the method viable, targeted sampling must be expanded, databases linking hydrogeochemistry, geology, and model outputs must be developed and made openly available, validated AI models must be created that can withstand scrutiny from both authorities and the environmental courts. With such initiatives, Sweden could streamline the search for new ore deposits, reduce exploration costs, and simultaneously strengthen environmental oversight.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".