Bibliographic record
Abstract
In this case study, we engage the novel Tri-Quarter framework by applying it to Binary Phase-Shift Keying (BPSK) signal processing, where we leverage structured orientation phase pair assignments and dynamic weight adjustments to enhance noise filtering and error correction under Gaussian and non-Gaussian noise. We attack the challenge of reliable decoding in communication systems like wireless networks, satellite links, and IoT devices, where noise varies from Additive White Gaussian Noise (AWGN) to impulsive noise (IN) interference. The framework implements a model-free methodology by using sign-based phase assignments and distance-based weights to decode signals without prior noise knowledge. Simulations at a signal-to-noise ratio (SNR) of 6 dB with 100,000 trials demonstrate that the Tri-Quarter framework's noise filtering achieves a 2.350% bit error rate (BER) in AWGN, closely matching standard thresholding with 1 CPU cycle, while its error correction with 3 transmissions per symbol yields a 0.138% BER in AWGN and 0.430% BER in IN, performing comparably to majority voting (0.149% BER in AWGN, 1.415% BER in IN) and significantly outperforming Gaussian-tuned soft-decision decoding (0.030% BER in AWGN, 12.769% BER in IN) in non-Gaussian conditions. With 17 CPU cycles for error correction, the Tri-Quarter framework balances efficiency and robustness, dominating in unpredictable noise environments (e.g. urban cellular wireless networks, industrial IoT networks, oceanographic sensor networks, and naval communication networks), though it is less optimal for ultra-low-power devices or Gaussian-dominated environments. This framework offers a versatile solution for modern communication challenges, with potential extensions to complex modulations like Quadrature Phase-Shift Keying (QPSK).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".