Bibliographic record
Abstract
Venture capital investments have reached the highest level since the dot-com days. Almost seven billion dollars was invested last quarter alone. While clean-tech deals hit a new low, security deals increased the most. Security is the new black. How should we spend the next billion? Meet the VCs and strategize on the future! Deepak Jeevankumar, partner at General Catalyst, focuses on investments in cloud computing, big data, data center infrastructure and clean energy. He has been with General Catalyst Partners since 2010, first in Boston and later in the firm's Palo Alto office and has been closely involved in our investments in DataGravity, Virtual Instruments and Sunglass. Prior to joining GC, Deepak worked at Sun Microsystems and was an intern at the Yale Investments Office. At Sun, he was involved in designing a few top 10 supercomputers in the industry and led the high performance computing architecture practice in the Asia-Pacific region. Deepak is a graduate of the National University of Singapore, earning a B.Eng. in Computer Engineering; the Singapore-MIT Alliance, earning a S.M. in Computer Science; and the Yale School of Management, earning an M.B.A. John M. Jack actively consults startups and is a board partner at Andreessen Horowitz. Most recently, JJ was the CEO of Fortify Software, which was acquired by Hewlett-Packard in 2010 and was the market leader in protecting enterprises from the threats posed by security flaws in business-critical software applications. Prior to this, JJ was the CEO of Covalent (acquired by VMware), the COO of The Vantive Corporation (acquired by PeopleSoft) and held executive positions at Sybase Inc. JJ is on the boards of CipherCloud, ClearSlide, AlienVault and Cenzic.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.426 | 0.225 |
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".