Capacity indicator and load measuring system for aerial devices
Bibliographic record
Abstract
New working techniques on the distribution network, such as transformer lifting and installation, are increasing the need to maximize the capability of aerial devices. Hydro-Quebec's research institute, IREQ, thus developed the Capacity Indicator and Load Measuring System (CILMS) after three years of R&D efforts aimed at increasing the safety of articulated aerial lifts and validating new work-related methods. The embedded system, located near the bucket, displays the available load capacity and the actual load on the jib. The available capacity is based on the lift geometry. Sensors located on the lower and upper boom and on the jib transmit the angles and length to a microprocessor which processes the information, and then retrieves and displays the capacity in real time. The actual load on the jib is measured at the tip of the jib with a tool interface made up of two load pins. Any load lifted with the jib is calculated in terms of amplitude and angle, after which the amplitude is displayed in real time. Radio communication is used for lower boom angle and load pin measurements to preserve the lift's dielectric insulation. A signal is transmitted to a visual and audible alarm whenever the load exceeds the capacity. If an alarm sounds, the system then records the date and time, the geometry of the lift, and the load. These data are easily read and the operating parameters can be viewed or modified using the pushbuttons on the display unit.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".