Bibliographic record
Abstract
The electric vehicle producer saw deliveries fall below target as a result of 'short-term production challenges'. Tesla Motors Inc. reported Q4 2016 deliveries of 22,200 vehicles, meaning it missed its H2 target of 50,000 vehicles by 3,300 vehicles. The Q4 figure marks a 9% drop on Q3, when the automaker delivered 24,500 vehicles. Tesla CEO Elon Musk. (Source: JD Lasica) In a statement, the company said short-term production meant Q4 vehicle production was weighted more heavily towards the end of the quarter than planned. While it recovered enough to hit its production goal, the delay in production in challenges that impacted quarterly deliveries, including, among other things, cars missing shipping cutoffs for Europe and Asia, Tesla noted. In total this resulted in 2,750 vehicles missing delivery during the quarter. Tesla produced 24,882 vehicles during the quarter, bringing total 2016 production to 83,922 vehicles, a 64% increase on 2015. Tesla has garnered a lot of publicity recently, bringing production online at its Reno, Nevada, US battery gigafactory in January. China continues to lead the market for EVs in terms of both supply and demand, however. It recently confirmed its 2017 EV subsidy programme, prompting speculation that lithium prices may rise again. At the Advanced Automotive Battery Conference in Mainz, Germany in February, participants heard over 600,000 EVs were sold globally in January to November 2016.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".