Bibliographic record
Abstract
In the fast expanding world of technology, the internet has become an essential backbone in transferring data and information to business's and markets world-wide.With data needed to be transferred to countries oceans apart on a daily basis, a medium to transport this large volume of data at a high rate is required for global economies to perform at maximum efficiency.The South East Asia-Middle East-West Europe 4 (SEA-ME-WE 4) fibre optic sub-sea cable is such a medium capable of transporting large volumes of data on a global scale within the blink of an eye, making it ideal for global economies to conduct business with efficiency.The SEA-ME-WE 4 sub-sea cable currently spans 20,000 km linking South East Asia, The Middle East, North-East Africa and Western Europe.The fibre optic system was constructed using a Cable Ship and equipped with a Sea Plough designed to dig trenches, lay cable in the trenches and cover the cable under the sea bed.The system uses state-of-the-art Terabit technology to achieve ultra-fast data transfer (1.28 Terabits/second), and supports a wide range of communication media from telephone, internet, multimedia and various broadband applications.The SEA-ME-WE 4 consortium, comprised of sixteen international telecommunication companies, currently performs maintenance on the system, providing Cable Repair Ships to respond to areas where the cable has been cut or disrupted.The following paper will highlight the construction of the SEA-ME-WE 4 cable, the procedures used in laying the cable, the impact the fishing industry has on submarine cables like SEA-ME-WE 4 and how to prevent submarine cable damage.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".