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Record W4394147211 · doi:10.6084/m9.figshare.14320893

Vale S.A.: Cobalt Streaming

2021· dataset· en· W4394147211 on OpenAlexaboutno aff
Flávia D'Albergaria Freitas, Carlos Heitor Campani, Viktor Nigri Moszkowicz, Raphael Moses Roquete, Flávia Schwartz Maranho

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCobaltComputer scienceChemistry

Abstract

fetched live from OpenAlex

ABSTRACT The case “Vale S.A. — Cobalt Streaming” describes a transaction that has taken place at Vale, a Brazilian mining company, and one of the most important in its segment. The transaction was developed to de-risk an important project for an expansion of a mine in the province of Newfoundland & Labrador in Canada. By streaming the cobalt production, Vale was able to get a competitive internal rate of return for the project compared to the lower level of risk the project would then offer. The case detailed the negotiation since the beginning until the company faced the challenge of choosing from the final proposals. The case allows the discussion of important aspects regarding project valuation: risk mitigation through the streaming negotiation, several different types of risk influencing the main issue of the case, decisions about the assumptions used, discussion about debt/equity characteristics on the overall project from Vale’s perspectives, and the evaluation of a project with non-conventional cash flow, given a substantial upfront revenue due to the streaming contract. So, the case is recommended for the disciplines of Financial Management, Project Valuation, or Risk Analysis in post-graduate courses of Business Administration and Finance.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.063
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0630.054

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 head0.059
GPT teacher head0.236
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2021
Admission routes1
Has abstractyes

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