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Record W6989121008

Aktuella affärsmodeller inom litiumprospektering

2022· article· en· W6989121008 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2022
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsLicenseLegislationProcess (computing)Task (project management)Product (mathematics)Renewable energyBusiness risksSustainable developmentProduction (economics)
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.240
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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