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286534 occurrences (No.87 in the rank) during 5 years in the PubMed. [cache]

15) The fit statistics of the models (based on DBH and DBH-H explanatory variables) obtained for Quercus frainetto are very close to each other, and they explained the variation in BT at a rate of 69% and estimated with an error rate of 26%.

--- ABSTRACT ---

PMID:34657980 DOI:10.1007/s10661-021-09524-x

2021 Environmental monitoring and assessment

* Bark thickness models for oak forests being converted from coppice to high forests in Northwestern Turkey.

- The research was carried out in the coppice-originated pure oak stands that are being converted to high forests in northwest Turkey. The main goal of the research was to determine the bark thickness (BT) based on tree variables, such as tree diameter at breast height (DBH), total tree height (H), crown diameter (CD), and age (AGE) of the stem sections taken from a total of 350 trees that were destructively sampled from different sites, different oak species (Quercus petraea, Quercus frainetto, Quercus cerris), and different development stages. Models were developed with stepwise multiple regression analysis to predict BT based on the variables. For all oak species, all models obtained by stepwise multiple regression analysis were found to be significant at p = 0.001 level. In Quercus petraea, only the DBH-dependent model explained the variation in BT at a rate of 73%, estimating with an absolute error rate of 21%. The fit statistics of the models (based on DBH and DBH-H explanatory variables) obtained for Quercus frainetto are very close to each other, and they explained the variation in BT at a rate of 69% and estimated with an error rate of 26%. Models (based on DBH and DBH-H explanatory variables) explain the variation in BT in Turkey oak at a rate of 91%, indicating species-specific results. The models based on only DBH can be used with high accuracy to estimate BT.

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[frequency of next (right) word to rate]

(1)172 of	(10)5 that	(19)3 step	(28)2 increased
(2)40 and	(11)5 the	(20)2 (ESR),	(29)2 it
(3)32 was	(12)4 (HR),	(21)2 (RMR)	(30)2 lactate,
(4)31 null	(13)4 among	(22)2 after	(31)2 or
(5)24 in	(14)4 discrimination	(23)2 at	(32)2 respiratory
(6)8 variability	(15)4 is	(24)2 attributable	(33)2 to
(7)6 constants	(16)4 were	(25)2 between	(34)2 top
(8)6 for	(17)3 enzyme	(26)2 constant	(35)2 which
(9)5 (HR)	(18)3 on	(27)2 equations

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--- WordNet output for rate --- =>価格を評定する, 割合, 率, 歩合, 料金, 値段, 相場, 速度, 度合, 人を〜を思う, みなす, 価値がある, 人を〜とみなす, 科金, 評価する Overview of noun rate The noun rate has 4 senses (first 3 from tagged texts) 1. (68) rate -- (a magnitude or frequency relative to a time unit; "they traveled at a rate of 55 miles per hour"; "the rate of change was faster than expected") 2. (39) rate, charge per unit -- (amount of a charge or payment relative to some basis; "a 10-minute phone call at that rate would cost $5") 3. (1) pace, rate -- (the relative speed of progress or change; "he lived at a fast pace"; "he works at a great rate"; "the pace of events accelerated") 4. rate -- (a quantity or amount or measure considered as a proportion of another quantity or amount or measure; "the literacy rate"; "the retention rate"; "the dropout rate") Overview of verb rate The verb rate has 3 senses (first 3 from tagged texts) 1. (9) rate, rank, range, order, grade, place -- (assign a rank or rating to; "how would you rank these students?"; "The restaurant is rated highly in the food guide") 2. (2) rate -- (be worthy of or have a certain rating; "This bond rates highly") 3. (1) rate, value -- (estimate the value of; "How would you rate his chances to become President?"; "Gold was rated highly among the Romans") --- WordNet end ---