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In mathematics, a G-measure is a measure that can be represented as the weak-∗ limit of a sequence of measurable functions = =. A classic example is the Riesz product G n ( t ) = ∏ k = 1 n ( 1 + r cos ( 2 π m k t ) ) {\displaystyle G_{n}(t)=\prod _{k=1}^{n}\left(1+r\cos(2\pi m^{k}t)\right)}
Expressed in grams (g) per square metre (g/m 2), regardless of its thickness [1] (known as grammage). This is the measure used in most parts of the world. It is often notated as gsm on paper product labels and spec sheets. Expressed in terms of the mass per number of sheets of a specific paper size (known as basis weight).
Ranking of query is one of the fundamental problems in information retrieval (IR), [1] the scientific/engineering discipline behind search engines. [2] Given a query q and a collection D of documents that match the query, the problem is to rank, that is, sort, the documents in D according to some criterion so that the "best" results appear early in the result list displayed to the user.
Measure G would expand the L.A. County Board of Supervisors to nine members from its current five: from left, Janice Hahn, Hilda Solis, Lindsey Horvath, Kathryn Barger and Holly Mitchell.
Offline metrics are generally created from relevance judgment sessions where the judges score the quality of the search results. Both binary (relevant/non-relevant) and multi-level (e.g., relevance from 0 to 5) scales can be used to score each document returned in response to a query.
Objectives and key results (OKR, alternatively OKRs) is a goal-setting framework used by individuals, teams, and organizations to define measurable goals and track their outcomes. The development of OKR is generally attributed to Andrew Grove who introduced the approach to Intel in the 1970s [ 1 ] and documented the framework in his 1983 book ...
Discounted cumulative gain (DCG) is a measure of ranking quality in information retrieval. It is often normalized so that it is comparable across queries, giving Normalized DCG (nDCG or NDCG). NDCG is often used to measure effectiveness of search engine algorithms and related applications.
"The Flesch–Kincaid" (F–K) reading grade level was developed under contract to the U.S. Navy in 1975 by J. Peter Kincaid and his team. [1] Related U.S. Navy research directed by Kincaid delved into high-tech education (for example, the electronic authoring and delivery of technical information), [2] usefulness of the Flesch–Kincaid readability formula, [3] computer aids for editing tests ...