A Study on the Minimum and Maximum Sum of C2 Problem in IMO2014
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The focus of this paper is primarily on a problem: the principle of the extreme value under some special operations. After enumerating from the maximum sum to minimum and solving these cases, I found that the use of the two mathematical models enabled the derivation of the general form of the use of the two mathematical models enabled the derivation of the general form of the maximum and the minimum sum. This program looks into the principles of minimum and maximum sum, and the various patterns that come along with it. In order to further discuss this kind of problems, we set up other different conditions, solving them with two mathematical models and principle of sequence recursive relationship, induction proof, etc. We also extend all these problems to explore the generating functions of the maximum and the minimum sum with operating number m based on the parity of the number of papers. Finally, using computer generated software, we demonstrate the various sums of a particular state, along with coming up with a general rule for all states that can predict the maximum and the minimum sum through the usage of induction.
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.
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 | 0.004 | 0.001 |
| 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 it