Bibliographic record
Abstract
INTERTECH 2000论文集中关于超硬材料在航空领 域应用的论文共有14篇。分别介绍如下： 1.超硬材料及其制品在航空工业中的应用。作者美国GE公司市场销售部经理Terry Kane。论文探讨了超硬材料制品及工具在航空航天中的应用。作者对航空航天技术的发展、金属材料的加工及CBN和金刚石两种超硬材料的基本性能、使用范围作了概括性的介绍。并针对航空领域中的几种典型材料，如高温高强合金、钛合金、金属基复合物、碳钎维材料、陶瓷、超硬钢、高塑性材料、热喷嘴材料和记忆合金的性能要求及加工时所选择的超硬材料工具进行了讨论,指出了不同材料对加工用超硬工具的不同要求。最后，作者在分析航空材料加工的基础上，进一步分析了超硬材料及其应用对整个行业的影响。全文图29幅。
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".