Bibliographic record
Abstract
This is the sixt beta of v1.12, which brings a number of significant improvements to PX4: The full release notes are here: https://docs.px4.io/master/en/releases/1.12.html Multi-EKF enabled by default Safety (switch) defaults to off (motors are disarmed, but servos / flaps can move) Safety switch is latching: Once it is disabled, it will stay disabled Multicopter Intuitive stick feel in Position mode Hover thrust independent velocity control gains UAVCANv0: Although the fundamental features like Firmware upgrades and parameter sync of CAN nodes have been implemented for over 5 years, we refreshed support now that finally, devices are on the market. Typical CAN GPS, airspeed and power modules are supported UAVCANv0 Node: PX4 supported building nodes for many years - now we support building specific targets like the CUAV GPS units UAVCANv1: Initial alpha of a complete end-to-end implementation Fixedwing/VTOL significant TECS improvements Magnetometer calibration faster and more robust new soft iron calibration coefficients automatically determine the rotation of external sensors Gyro dynamic notch filtering with onboard FFT Optimized rate control sensor pipeline (minimal inner loop end-to-end latency) Added support for IRC Ghost including telemetry new board support PX4 FMUv6u PX4 FMUv6x CUAV X7/X7Pro CUAV Nora CUAV CAN GPS SP Racing H7 Extreme Bitcraze Crazyflie v2.1 ARK CAN Flow mRo Ctrl Zero H7 (experimental) Differences to Beta 3: Fixed a rate mismatch on PWM outputs on FMU
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.405 | 0.343 |
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".