Continuous-Time Stationary Processes And Wind Power:Infinitely divisible distributions, stochastic delay differentialequations, and applcations to wind power production
Bibliographic record
Abstract
half of my PhD studies and are therefore included to a varying extent in my progress report written as part of the qualifying examination after which I obtained a master's degree in Mathematics-Economics.The first chapter is an introduction to the studied topics.A small introduction to each paper is also included.The introduction is meant to tie the papers together under three main themes: infinitely divisible distributions, stochastic delay differential equations, and wind power production.While these themes cover a variety of very different topics, it is an aim of the introduction to unveil some common ground among them.The papers appear ordered in the continuum introduced by the three main themes, ranging from infinitely divisible distributions over stochastic delay differential equations to wind power production.In this way, Paper A is the paper most concerned with infinitely divisible distributions and Paper I deals most exclusively with wind power production.This progression of the papers through the main themes is not chronological.Together with the conclusion of my PhD studies belongs a sincere thanks to a lot of people.I wish to express my gratitude to my supervisor Andreas Basse-O'Connor for many insightful comments, considerate attitude, and cheerful mood.My other supervisor Jan Pedersen also deserve a big praise for always having an open door, asking the good question, and having an abundance of helpful comments both in and outside the academic setup.I am truly grateful to both my supervisors for showing patience with me, for the guidance through the years, and the many meetings with advise and laughter.I would like to thank Fred Espen Benth for being the central figure in a very pleasant visit to the University of Oslo.I enjoyed my time there immensely, and the conversations in Oslo and the following correspondence via email have always been insightful and enjoyable.Our collaborations have inspired me to pursue different areas of mathematics, and have therefore sparked a lot of passion in me.I would also like to express my gratitude to Mikkel Slot Nielsen for the many interesting collaborations and discussions.Furthermore, a warm thank you goes out to Troels Sønderby Christensen for a very pleasant collaboration and visits, both the visit to Aarhus and when I went to Aalborg, and for the good times in the office and on the skis in Oslo.James Nichols and Vestas Wind Systems also deserve a big thank you for a great collaboration and interesting meetings.Many fellow PhD student at Aarhus University also deserve a big praise for the many joyful experiences over the years, in particular, Julie, Thorbjørn, Mikkel, Jeanett, Patrick, Mads, Claudio, and Mathias.Finally, my most heartfelt thanks goes to my family.I am deeply grateful to my significant other, Line, who is
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".