Bibliographic record
Abstract
Remote access laboratories (RALs) were first developed in 1994 in Australia and Switzerland. The main purposes of developing them are to enable students to do their experiments at their own pace, time and locations and to enable students and teaching staff to get access to facilities beyond their institutions. Currently, most of the experiments carried out through RALs in Australia are heavily biased towards electrical, electronic and computer engineering disciplines. However, the experiments carried out through RALs in Europe had more variety, in addition to the traditional electrical, electronic and computer engineering disciplines, there were experiments in mechanical and mechatronic disciplines. It was found that RALs are now being developed aggressively in Australia and Europe and it can be argued that RALs will develop further and faster in the future with improving Internet technology. The rising costs of real experimental equipment will also speed up their development because by making the equipment remotely accessible, the cost can be shared by more universities or institutions and this will improve their cost-effectiveness. Their development would be particularly rapid in large countries with small populations such as Australia, Canada and Russia, because of the scale of economy. Reusability of software, interoperability in software implementation, computer supported collaborative learning and convergence with learning management systems are the required development of future RALs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".