Lectures on Ane Algebraic Geometry
Bibliographic record
Abstract
These notes were taken during a course given by Prof. Adrian Iovita during the 2007 Fall semester at Concordia University (Montreal). It is meant to be a rst course in Algebraic Geometry from the scheme-theory point of view, introducing the commutative algebra when it is needed in the discussion. The course is mainly self-contained, and assumes a basic knowledge of commu-tative algebra, although most of the results are proved in the course. It is somewhat non-standard in the choice of topics, as it moves fast towards etale cohomology, which will be the subject of the second course. This means that a lot of fundamental theory is skipped, but also it allows one to see things more dicult to nd in other introductory texts. If it followed an available text, that would be [2], but it really goes its own way. I have tried to write almost all that was said in the lectures, maybe omitting some simple proofs that are best done as exercises. It is very advisable as well to do all the exercises that are suggested, in order to follow the exposition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.036 |
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".