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[−1,1]
Why this formula appears here
Define the channel-attributable transfer rate for exposure class c∈\{V,H\} as Tc=1−I0Iˉc−I0 . This is a direct borrowing from the attributable-risk fraction used in outbreak epidemiology, where it measures what share of disease in an exposed group would disappear if the exposure were removed; here it measures what share of a model’s trait incidence would disappear if its exposure to the source artifact were removed, holding everything else about the model constant. Tc=0 means the exposed class shows the trait no more often than baseline: no signal. Tc=1 means every trait-negative case in the exposed class would have been positive under full exposure: the strongest…
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Equation 26 · Evolutionary AI
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.
Define the channel-attributable transfer rate for exposure class c∈\{V,H\} as Tc=1−I0Iˉc−I0 . This is a direct borrowing from the attributable-risk fraction used in outbreak epidemiology, where it measures what share of disease in an exposed group would disappear if the exposure were removed; here it measures what share of a model’s trait incidence would disappear if its exposure to the source artifact were removed, holding everything else about the model constant. Tc=0 means the exposed class shows the trait no more often than baseline: no signal. Tc=1 means every trait-negative case in the exposed class would have been positive under full exposure: the strongest…
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Article →Equation 1 · AI Agents & Systems
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.
The fix is hybrid retrieval: combine a lexical score, which is exact-match strong and semantically blind, with a dense score, which is semantically strong and exact-match weak. The complication is that the two scores are not on comparable scales. Pinecone’s documentation states the problem directly: dense vectors scored by inner product against unit-normalized embeddings fall roughly in the range [-1, 1] , while BM25-style sparse scores are unbounded positive values that grow with term frequency, document length, and vocabulary rarity, so that “without explicit weighting, the sparse component dominates the combined score” [ 4 ] . The documented fix is a convex combination of normalized…
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