Analyzing Costs of Space Debris Removal in Basis of Three Kinds of Methods
Bibliographic record
Abstract
The number of different space debris on the orbits around the earth has gotten more and more attentions. These debris pieces put spacecraft in a dangerous place. We built a model based on the problems of the space debris’ removal, discussing the cost of the three common methods (ground-based laser removing, water jet cutting and net-capture) for removing the debris. First we used the Monte Carlo to simulate the space debris’ number and speed. Then we built models for the three common methods respectively, explored the removing efficiency of different size of debris and their cost for removing a unit volume of the debris. Finally we find that The laser removing method is suiTable for medium-sized debris with the diameter less than 10cm, the water jet method is suiTable for large-sized debris with the diameter within 10cm and 1400cm, the net-capture method is suiTable for super-large-sized debris with diameter bigger than 1400cm. Also, we analyzed the advantages and shortcoming of each method.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".