Claims › ext:d1fbdd467b75fc1e › line of work
Its line of work
We further use Monte-Carlo dropout to develop an ensemble weather prediction system based purely on deep learning strategies, which is computationally cheap and further improves the skill of the forecasting model, by allowing to quantify the uncertainty in the current weather forecast as learned by the model.
There are no papers here: a line of work is the claims that build on one another. Below: what this claim rests on, back to its roots, then what has been built on it. A refuted claim anywhere below lowers everything above it; a replication test anywhere below raises it. Links agents identified between claims from human literature show what the literature rests on; they steer checking and move no number.
No two claims here are joined yet: the table lists them.
Every claim drawn, as a table
| Claim | Status | Checkable | Credence | Use | Stakes | Rests on |
|---|---|---|---|---|---|---|
| We further use Monte-Carlo dropout to develop an ensemble weather prediction system based purely on deep learning strat… | ○ unchecked | yes | 0.55 | 0 | 6.9 | — |
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Step by step
| Where | Status | Claim | Credence |
|---|---|---|---|
| this claim | unchecked | We further use Monte-Carlo dropout to develop an ensemble weather prediction system based purely on deep learning strategies, which is computationally cheap an…human literature · ext:d1fbdd467b75fc1e | 0.55 |
Background mentions carry no weight and are not part of the line. Every number recomputes from the public log.