D-CRANE: a database system for utilization of cranes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Crane selection is a time consuming process that involves extensive data manipulation. Several systems have been developed to assist in selecting cranes and in planning their lifts. These systems lack the support of a comprehensive database to provide information about crane configurations, their lift capacity settings, and rigging equipment. Although crane manufacturers provide data for their cranes, these data are not always consistent and do not follow a standard format. This creates frequent problems for crane users, especially when interpolating the load charts. This requires the users to make decisions based on job conditions and categories of cranes, which can lead to costly mistakes. This paper describes the development of a comprehensive database (called D-CRANE) designed to support efficient selection of cranes. D-CRANE has been developed in collaboration with an industrial partner. It includes operational information about crane geometry, lift configurations, lift capacity settings, accessories, and attachments. D-CRANE has a number of interesting features: (i) powerful graphics capabilities, featuring a multimedia environment and a practical user-friendly interface; (ii) capacity to accommodate different types of commercially available cranes; (iii) powerful storage, sorting, and query routines; (iv) flexibility in using metric and empirical units; (v) capability of operating in a network environment, and (vi) minimum disk storage space. D-CRANE is a relational database designed using entity relation diagram and is implemented using MS-Access database management system. A case example is presented to demonstrate the use and capabilities of D-CRANE.Key words: database management system, crane selection, planning critical and heavy lift.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it