Bibliographic record
Abstract
Teekay Shuttle Tankers L.L.C. ("Teekay") is a globally operating owner and operator of shuttle tankers and headquartered in Hamilton, Bermuda.Teekay transports oil from offshore fields to the shore by specialized tankers.Teekay's customer base consists of primarily oil majors and producers.Teekay is operating a fleet of 33 shuttle tankers mainly in the North Sea, Brazil and the East Coast of Canada.Proceeds under this framework finance or refinance shuttle tankers.Eligible assets are E-Shuttles with an expected lifetime of 20 years, in whole or in part, that are powered by battery hybrid technology, LNG and condensed Volatile Organic Compounds (VOC, crude oil vapors) as an LNG additive.With the first issuance four vessels will be financed.According to the issuer, each shuttle features annual CO2 savings of 47% (23 200 tCO2) as well as an 88% NOx and a 99% SOx and 95% VOC emissions reduction compared to business-as-usual in the North Sea context.CICERO Green welcomes innovation in the oil based shipping industry that has the potential to accelerate the adoption of lower emission technology also in the wider shipping industry.E-Shuttle technology was partly funded by the Norwegian government entity ENOVA which have a clear mandate to reduce carbon emissions.In this context CICERO Green views Teekay's investments in significantly more efficient shipping as important shorter term steps to reach the well below 2°C target. E-Shuttles have been acquired to directly replace older, conventional vessels of Teekay's fleet based on already committed field developments and can be convertedto transport other goods (e.g., bio diesel, potable water etc.) should oil production decrease.However, the shuttles transport an estimate of 23.5 million barrels of crude oil with associated emissions of 9.4MtCO2 -more than the total emissions from road traffic in Norway in 2018.In the case of decreased oil production, Teekay will likely phase out conventional vessels first.This points towards long-term emissions reduction of investments.The issuer does not exclude utilizing E-Shuttles for new field developments in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.685 | 0.492 |
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".