D-CRANE: a database system for utilization of cranes
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.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".