EFFECT OF RELATIVE REINFORCEMENT DURATION IN CONCURRENT SCHEDULES WITH DIFFERENT REINFORCEMENT DENSITIES: A REPLICATION OF DAVISON (1988)

Previous studies have challenged the prediction of the Generalized Matching Law about the effect of relative, but not absolute, value of reinforcement parameters on relative choice measures. Six pigeons were run in an experiment involving concurrent variable-interval schedules with unequal reinforcer durations associated with the response alternatives (10 s versus 3s), a systematic replication of Davison (1988). Programmed reinforcement frequency was kept equal for the competing responses while their absolute value was varied. Measures of both response ratios and time ratios showed preference for the larger duration alternative and that preference did not change systematically with changes in absolute reinforcer frequency. Present results support the relativity assumption of the Matching Law. It is suggested that Davison’s results were due to uncontrolled variations in obtained reinforcement frequency. Keywords: choice, preference, overall reinforcer frequency, reinforcer magnitude, pigeons.

Positive correlation between fluoride release and acid erosion of restorative glass-ionomer cements.

OBJECTIVE: The aim of this study was to determine whether there is a correlation between acid erosion and fluoride release of conventional glass ionomer cements. METHODS: Ten specimens for each material were prepared for fluoride release tests and five for acid erosion tests separately. After placed in pH cycling solution, concentration of fluoride was measured by a fluoride-ion selective electrode each day for 15 days. For the acid erosion test, specimens were immersed in a lactic acid solution and their depth measured with a spring-loaded dial gauge. The data were submitted to 3-way ANOVA, followed by Tukey's test (p0.05). The highest acid erosion values were registered for Magic Glass, Ion Z, VitroFil and Maxxion R, which exceeded the maximum stipulated by the relevant ISO test (ISO 9917-1). A positive linear correlation (r2=0.4886) was found for both properties, i.e., higher fluoride release is related to higher acid erosion. SIGNIFICANCE: Acid erosion and fluoride release are related properties of GICs, though factors such as pH and P/L ratio lead to differences between actual values for individual brands of these materials

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost