Novel Optimal Perennial Calendar Systems vs Gregorian Calendar and Their Impact on the Energy Demand and the Environment
Bibliographic record
Abstract
Have you ever missed an event because you were confused about days and dates? Do you remember the date of any specific day without looking at the calendar? Is the current Gregorian Calendar efficient enough for usage, and does it facilitate our life or make it more complicated? Have you ever thought about a simpler way to calculate days and dates in a year without using a calendar? All these questions are answered in this paper, in which authors propose two contributions, (a) a new mathematical formula that calculates the number of days in any month in the Gregorian calendar for any year, including the leap years, (b) an original optimization method that creates optimal perennial calendars. Results show that there is more than one way to create a perennial calendar using the proposed optimization model, in which the number of days in each month does not change, neither the dates. Hence, all months have the same sequence of days and dates. In other meaning, Monday becomes the first day of every month, and Sunday becomes the last day. Consequently, the calendars become much easier to memorize, and it becomes simpler to predict the days and dates in any year. In addition, the proposed optimal perennial calendar system reduces the energy demand and pollution worldwide, in which it has less impact on the environment and climate change compared to the Gregorian calendar. This is due to the fact that less printed-out calendars are produced, and less time is spent on the digital calendars to check the dates and days.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".