Experimental Instructional Design of Code Slicing and Data Verification by Artificial Interruption
Bibliographic record
Abstract
Students run code directly without any modification when doing programming experiments. They do not care about the process and do not understand the results. It has always been the pain of the electronic information experiment teaching mentioned by. This article, combines the previous teaching experience, proposes an artificial serial port interruption in the code. The result data returned by the interrupt upload to computer or mobile phone, combined with the experimental principle to allow students to explore the relationship between input and output, so that students can fully participate in the modification of code parameters, and verify the intermediate results. Students connect to the WIFI hotspot configured in the experiment box through their mobile phones, and lunch the online commissioning APP serial port configuration interface to complete the basic serial port settings. The slave MCU needs to download the firmware code compiled in C language in advance. The code contains the serial port output function to calibrate the running position of the code and upload the specified operation results. This article briefly describes the hardware functional chart and the functional flowchart of the interrupt code; focusing on the interface design and interrupt implementation method of online commissioning APP, as well as the test environment setup and specific test results with serial port tools.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".