The prediction of consumer buying intentions : a comparative study of the predictive efficacy of two attitudinal models / 234

Includes bibliographical references (p. 26-29)

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

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Not AvailableSoils in the hot, arid topical regions are low in organic matter and fertility and are structurally poor. Consequently, these soils suffer on account of poor physical, chemical, and biological soil quality traits, leading to miserably low crop yields. Long-term use of conjunctive nutrient management and conservation tillage practices may have a profound effect on improving the quality of these soils. Therefore, the objective of this study was to identify the key soil quality indicators, indices, and the best soiland nutrient-management practices that can improve soil quality on long-term basis for enhanced productivity under a pearl millet–based system. The studies were conducted for the Hissar Centre of All-India Coordinated Research Project at the Central Research Institute for Dryland Agriculture, Hyderabad. Conjunctive nutrient-use treatments and conservation tillage significantly influenced the majority of the soil quality parameters in both the experiments. In experiment 1, the key soil quality indicators that significantly contributed to soil quality in a rainfed pearl millet–mung bean system were available nitrogen (N, 35%), available zinc (Zn; 35%), available copper (Cu; 10%), pH (10%), available potassium (K; 5%), and dehydrogenase assay (5%). The three best conjunctive nutrient-use treatments in terms of soil quality indices (SQI) were T3, 25 kg N (compost) (1.52) >T6, 15 kg N (compost) + 10 kg N (inorganic) + biofertilizer (1.49) >T5, 15 kg N (compost) + 10 kg N (green leaf manure) (1.47). In experiment 2, under a rainfed pearl millet system, the key indicators and their percentage contributions were electrical conductivity (15%), available N (19%), exchangeable magnesium (Mg; 18%), available manganese (Mn; 13%), dehydrogenase assay (19%), microbial biomass carbon (C; 5%), and bulk density (11%). The three best tillage +nutrient treatments identified from the viewpoint of soil quality were T1, conventional tillage (CT) + two intercultures (IC) + 100% N (organic source/compost) (1.74) >T3, CT + two IC + 100% N (inorganic source) (1.74) >T4, low tillage + two IC + 100% N (organic source/compost) (1.70). The findings of the present study as well as the state-of-the-art methodology adopted could be of much interest and use to the future researchers including students, land managers, state agricultural officers, growers/farmers, and all other associated stakeholders. The prediction function developed between long-term pearl millet crop yields (y) and soil quality indices (x) in this study could be of much use in predicting the crop yields with a given change in soil quality index under similar situations.Not Availabl