Bibliographic record
Abstract
This paper discusses the excavation phase of seafloor massive sulphide mining. The excavator is part of an overall mining system that will also include a vertical riser to lift the excavated material to the surface, and a shipboard dewatering plant to minimize the loss of fine particles back into the water. External variables affecting the excavator performance include rock properties and terrain. Given a set of rock properties and a desired production rate, we can choose a cutter type and we can estimate the required forces, torques, and power requirements. Typically we can use marinized versions of existing land-based rock cutters for this application. However, we need to pay particular attention to the design of the cutterhead assembly - we need to control the flow field so that we can lift excavated material into the riser and not leave it on the bottom or lose it into plumes. Rock cutters can be deployed in multiple modes, i.e. transverse cutting, sumping, and trenching. The design of the excavator as a system needs to consider the cutting mode and the method of advance of the excavator platform. Examples of excavator advance methods include "lawn mowing", where the cutterheads continuously excavate as the machine advances, "scything", where the cutterhead takes horizontal swaths as the excavator advances, and "open pit mining" where the machine excavates a steep face in front of itself. Each of these modes has advantages and disadvantages in terms of machine size, wear, and time efficiency. Finally, the interface to the riser is important in terms of decoupling vessel and riser motions from the excavator and whether or not the excavator can be mated and unmated to and from the riser while on the bottom.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".