Fluid Level Measurement System in Oil Storage. Python, Lab-Based Scale
Bibliographic record
Abstract
Refineries, fuel depots, airports and storage terminals commonly use fluid level measurement in storage tanks. Different fluid level measurement techniques and devices differ in the inaccuracy of measurement results, costs, and company needs. In addition, these measurements seek reliability of measurement data, immediate response times, control in operations, oil movement, custody transfer and inventory control. The objective is to develop a computer system for measuring fluid levels in oil storage tanks, using ultrasonic and temperature sensors, creating a web application for an automated measurement system (SAM) for managing volumes of Petroleum. The study methodology is i) Analysis of measurement reports. ii) Selection of physical components of the computer system. iii) SAM algorithm design and web application, and iv) Validate the system. The SAM application developed in open source proposes functional modules for administration, control, security, management and monitoring of storage tanks, the status of physical components and generation of dynamic reports in real-time. The results show the control characteristics of storage tanks such as maximum and minimum volume, temperature, time, precise data records in less time than certain current computer structures.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".