Bibliographic record
Abstract
Tesla is in the news again. Tesla Incorporation (Inc.) has well engineered cars with extensive power and nominal emissions which had helped Tesla's products to stand out and make a mark in this growing sector. Establishing its presence in the prominent markets of The United States, Europe, Asia and Canada, the reach of Tesla Inc. has been creditable. The gradual shift of the consumers towards the importance of environment-friendly automobile options has helped to facilitate this. Another reason why consumers seem to find the shift to electric cars feasible is the fact that consumers can now avoid the cumbersome process of fueling by going to a gas station. Instead, they can now charge their vehicles at home. But even after having potential market and new orders in the agenda of Tesla, Why the company ends up in declaring losses every year? This is a question in everyone's mind. This study is an attempt to find answer/s to this question through Financial Statements analysis taking last three financial years i.e. 2015-2017. The current research has adopted descriptive method of research through secondary data. Financial Statements has been downloaded from the official website of Tesla Inc. and prepared Comparative and Common-size statements along with 17 financial ratios. This study observed that Gross Profit for the Company was in increasing trend in absolute figures but when compared as a percentage of sales it reveals that Gross Profit has been decreased from 23% in 2015 & 2016 to 19% in 2017. Coupled with this higher costs of Maintenance, Research and Development, Selling, General and Administrative expenses have triggered the company towards Net Loss.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".