Aktuella affärsmodeller inom litiumprospektering
Bibliographic record
Abstract
The world is facing the challenge of transforming from fossil fuel dependant to a zero-emission econ-omy. This results in multi-fold mineral requirements for technologies such as wind turbines, solar pan-els, and electric vehicles; an exorbitant amount that cannot be fulfilled by recycling alone. In Europe, this challenge is accelerated more by the current Russian conflict in Ukraine and the understanding that not only do we need to decarbonise the European economy, but also become independent from Russian energy and minerals. This transition requires many raw materials and the faster the transition occurs; the more minerals are required to be mined for these important technologies. Before mining can occur, mineral deposits must be discovered during the process of exploration. The success rate of exploration is less than 1 mine from 1000 exploration projects and projects can take decades to convert from discovery to producing mine. On top of this, we are currently searching for lower-grade deposits that are more difficult to find and technically complicated to extract. All this at a time, when environmental legislation is becoming more strict, there’s a requirement for decarbonisation in the mining industry, and the social license to operate is more difficult to obtain. This seemingly impossible task brings into question the efficiency of the business model of exploration companies to determine whether business model innovation can help achieve a more environmentally, socially, and economically sustainable industry. This study analyses companies working in lithium exploration, as lithium is a material that is re-quired in significant amounts for the green energy transition. The number of companies operating in this sector has increased significantly in the past few years. Through qualitative content analysis using web content, a cross sectional study of 55 companies listed on the Toronto Stock Exchange was com-pleted to identify themes relating to the business models of each company. Eight overlapping innova-tion categories were identified in 29 of the companies including Environment, Social, Economic, Cir-cular, Collaborative, Lean, Technology and Value Chain innovation.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".