weighting scheme
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weighting scheme has 18 facts recorded in Dontopedia across 4 references, with 1 live disagreement.
Mostly:rdf:type(2), is neutral on dataset(1), does not help performance(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
algorithmTypeAlgorithm Type(1)
- Tf Idf
ex:TF-IDF
attestsToAttests to(1)
- Assistant Turn
ex:AssistantTurn
hasRelevanceLiftFromHas Relevance Lift From(1)
- Dataset 4000
ex:dataset-4000
providesProvides(1)
- Turn 6409
ex:turn-6409
providesGuidanceProvides Guidance(1)
- Assistant
ex:assistant
Other facts (17)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Weighting Scheme | [3] |
| Rdf:type | Algorithm | [4] |
| Is Neutral on Dataset | null | [1] |
| Does Not Help Performance | NASA Ims Bearing Dataset | [1] |
| Does Not Hurt Performance | NASA Ims Bearing Dataset | [1] |
| Prioritizes | Cost Priority | [2] |
| Implements Multi Criteria Decision Analysis | Mcdm Pattern | [2] |
| Has Bm25 Weight | 0.6 | [3] |
| Has Dense Weight | 0.4 | [3] |
| Described in | Turn 6409 | [3] |
| Applies to | Turn 6409 | [3] |
| Implies | remaining weight is 0.4 | [3] |
| Calculation | 0.6 + 0.4 = 1.0 | [3] |
| Constraint | weights sum to 1.0 | [3] |
| Provided Relevance Lift | 18 | [4] |
| Lift Unit | percent | [4] |
| Validated on | Dataset 4000 | [4] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (4)
ctx:discord/blah/watt-activation/part-515ctx:claims/beam/f77b59d7-50ae-459f-8fcc-4e7f57e516a2- full textbeam-chunktext/plain1 KB
doc:beam/f77b59d7-50ae-459f-8fcc-4e7f57e516a2Show excerpt
for option_name, score in sorted_options: print(f"{option_name}: {score}") if __name__ == "__main__": main() ``` ### Execution with Provided Data Let's execute the script with the provided data: ```python Enter the numbe…
ctx:claims/beam/f31ec550-ac01-40c6-8a46-4681e4ca6cfbctx:claims/beam/2b9cc40e-4d45-444b-b775-a81c9b036d4a- full textbeam-chunktext/plain1 KB
doc:beam/2b9cc40e-4d45-444b-b775-a81c9b036d4aShow excerpt
[Turn 6413] Assistant: Great to hear that you've found a weighting scheme that provides an 18% relevance lift for 4,000 searches. Applying this to a larger dataset of 25,000 hybrid queries should be straightforward, given that the underlyin…
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