Bibliographic record
Abstract
Data Owner: C. Gorenflo, I.Rios, L. Golab, S. Keshav Data Description: This dataset contains data collected through the University of Waterloo’s WeBike field trial, which includes e-bike trips and battery charging sessions spanning from summer 2014 until spring 2017. 31 participants were each given an e-bike, and the e-bikes are monitored by our custom-built kit. The Samsung Galaxy S-III smart phone provides the angular speed and acceleration in all 3 axes through Android’s standard API; Phidget voltage sensor measures the battery voltage, a Phidget current transducer measures the battery charging current, a Digikey current transducer measures the battery discharge current, and a Digikey sensor measures the temperature of the battery. The smart phones are configured the smart phones to wake up for 4 seconds every minute and collect four data samples, one per second, from all the sensors. More information can be found at: https://iss4e.ca/webike-software/ and https://iss4e.ca/webike-a-three-year-study-on-e-bikes-as-a-mode-of-sustainable-transport-in-a-canadian-city/ To identify trip activities and battery charging sessions from the raw data in webike.json, you can follow the activity detection algorithm provided in Section 4.1 of Usage Patterns of Electric Bicycles: An Analysis of the WeBike Project, (https://www.hindawi.com/journals/jat/2017/3739505/), and the scripts we used can be found at: https://github.com/iss4e. time: Timestamp of the data collected acceleration_x: Acceleration in x axis acceleration_y: Acceleration in y axis acceleration_z: Acceleration in z axis ambient_temperature: Ambient Temperature atmospheric_pressure: Atmospheric Pressure battery_temperature: Temperature inside box (from a sensor in the telemetry box, in Celsius) charging_current: Charging current (using Phidget sensor, is reported as a float value. To convert reported value to Amperes use: Charging Current (in A) = Sensor Value * 0.05. Therefore, a sensor value of 400 implies a charging current of 2 A) code_version: Version of our software stack source code discharge_current: Discharging current (using ISS4E built sensor): Discharge Current (A) = (Sensor Value-504)*0.033 (the value 504 is the value displayed by the sensor for 0 Amps; it can vary slightly, +/- few units, so adjust accordingly) gravitational_acceleration: Gravitational acceleration gyroscope_x: Angular speed in x axis gyroscope_y: Angular speed in y axis gyroscope_z: Angular speed in z axis light_level: Light level in lux linear_acceleration_x: Linear acceleration in x axis linear_acceleration_y: Linear acceleration in y axis linear_acceleration_z: Linear acceleration in z axis magnetic_field_x: Magnetic field in x axis magnetic_field_y: Magnetic field in y axis magnetic_field_z: Magnetic field in z axis phone_battery_state: The state the battery of the phone is in proximity_sensor: Proximity Sensor voltage: Battery voltage (using phidget sensor, in V): The actual voltage is 32/22 times the value read by the sensor phone_battery_percentage: Percentage of battery left phone_charging_or_full: Is phone charging or full phone_is_AC_charge: Does phone charge with AC adapter phone_is_USB_charge: Does phone charge with USB rotation_scalar: Rotation scalar rotation_x: Rotation in x axis rotation_y: Rotation in y axis rotation_z: Rotation in z axis Funding: Cisco Systems and the Natural Science and Engineering Research Council of Canada (NSERC).
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.323 | 0.486 |
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".