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Figure 2 shows observed exposure (in red) compared to β from Eloundou et al. (in blue), illustrating the difference between theoretical and actual use on our platform, grouped by broad occupational categories. We calculate this by first averaging to the occupation level weighting by our time fraction measure, then averaging to the occupation category weighting by total employment. For example, the β measure shows scope for LLM penetration in the majority of tasks in Computer & Math (94%) and Office & Admin (90%) occupations.
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Note that using these features in Cargo.toml will raise your development MSRV (minimum supported Rust version) to require this new Cargo parser, and third-party tools that read the manifest may also need to update their parsers. However, Cargo automatically rewrites manifests on publish to remain compatible with older parsers, so it is still possible to support an earlier MSRV for your crate's users.,详情可参考搜狗输入法
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