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Titlebook: Rarity; Kevin J. Gaston Book 1994 Kevin J. Gaston 1994 conservation.dynamics.population.temporal dynamics

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樓主: Malinger
31#
發(fā)表于 2025-3-26 23:55:48 | 只看該作者
Where next?,e continued explicit study of rarity. The second identifies some inadequacies in approaches to the study of rarity to date, emphasizing: (a) taxonomic bias, (b) the lack of comparative studies and their limitations, (c) the need to synthesize information from diverse literatures, (d) statistical complexities and (e) the role of experiments.
32#
發(fā)表于 2025-3-27 04:06:45 | 只看該作者
33#
發(fā)表于 2025-3-27 08:00:29 | 只看該作者
H. Riegerrue barrier against exogenous contamination. In certain cases, germfree delivery and rearing of humans is mandatory for those having congenital immune deficiencies to prevent contact with microorganisms which might cause serious or fatal infections.
34#
發(fā)表于 2025-3-27 10:23:11 | 只看該作者
Solid-Phase Microextraction and Comprehensive Two-Dimensional Gas Chromatography for Food Analysisn addition, guidelines for comprehensive two-dimensional gas chromatography (GC×GC) method development are also provided, especially when the technique is hyphenated with mass spectrometry (GC×GC-MS). To illustrate these good practices, commercial coffee capsules and brewing hops are presented as the model food samples.
35#
發(fā)表于 2025-3-27 15:00:57 | 只看該作者
AERM: An Attribute-Aware Economic Robust Spectrum Auction Mechanismce providers, and at the same time reuse spectrums. The economic robustness of the auction mechanism is of great importance. The auction mechanism proposed in this paper satisfies the three economic properties of truthfulness, individual rationality, and ex-post budget balance, so it is economically robust.
36#
發(fā)表于 2025-3-27 19:59:08 | 只看該作者
37#
發(fā)表于 2025-3-27 22:53:09 | 只看該作者
Margaret M. Smith,Phillip C. Heemstra) a novel, data-driven model based on a neural network capable of unsupervised learning of cancer growth curves. Employing mechanisms of competition, cooperation, and correlation in neural networks, GLUECK learns the temporal evolution of the input data along with the underlying distribution of the
38#
發(fā)表于 2025-3-28 05:35:02 | 只看該作者
39#
發(fā)表于 2025-3-28 07:04:20 | 只看該作者
40#
發(fā)表于 2025-3-28 10:41:00 | 只看該作者
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