title: Applying additive logistic regression to data derived from sensors monitoring behavioral and physiological characteristics of dairy cows to detect lameness

authors: Kamphuis, C

subjects: Science & Technology

abstract: The hypothesis was that sensors currently available on farm that monitor behavioral and physiological characteristics have potential for the detection of lameness in dairy cows. This was tested by applying additive logistic regression to variables derived from sensor data. Data were collected between November 2010 and June 2012 on 5 commercial pasture-based dairy farms. Sensor data from weigh scales (liveweight), pedometers (activity), and milk meters (milking order, unadjusted and adjusted milk yield in the first 2 min of milking, total milk yield, and milking duration) were collected at every milking from 4,904 cows. Lameness events were recorded by farmers who were trained in detecting lameness before the study commenced. A total of 318 lameness events affecting 292 cows were available for statistical analyses. For each lameness event, the lame cow’s sensor data for a time period of 14 d before observation date were randomly matched by farm and date to 10 healthy cows (i.e., cows that were not lame and had no other health event recorded for the matched time period). Sensor data relating to the 14-d time periods were used for developing univariable (using one source of sensor data) and multivariable (using multiple sources of sensor data) models. Model development involved the use of additive logistic regression by applying the LogitBoost algorithm with a regression tree as base learner. The model’s output was a probability estimate for lameness, given the sensor data collected during the 14-d time period. Models were validated using leave-one-farm-out cross-validation and, as a result of this validation, each cow in the data set (318 lame and 3,180 nonlame cows) received a probability estimate for lameness. Based on the area under the curve (AUC), results indicated that univariable models had low predictive potential, with the highest AUC values found for liveweight (AUC = 0.66), activity (AUC = 0.60), and milking order (AUC = 0.65). Combining these 3 sensors improved AUC to 0.74. Detection performance of this combined model varied between farms but it consistently and significantly outperformed univariable models across farms at a fixed specificity of 80%. Still, detection performance was not high enough to be implemented in practice on large, pasture-based dairy farms. Future research may improve performance by developing variables based on sensor data of liveweight, activity, and milking order, but that better describe changes in sensor data patterns when cows go lame.

text: Applying additive logistic regression to data derived from sensors monitoring behavioral and physiological characteristics of dairy cows to detect lameness The hypothesis was that sensors currently available on farm that monitor behavioral and physiological characteristics have potential for the detection of lameness in dairy cows. This was tested by applying additive logistic regression to variables derived from sensor data. Data were collected between November 2010 and June 2012 on 5 commercial pasture-based dairy farms. Sensor data from weigh scales (liveweight), pedometers (activity), and milk meters (milking order, unadjusted and adjusted milk yield in the first 2 min of milking, total milk yield, and milking duration) were collected at every milking from 4,904 cows. Lameness events were recorded by farmers who were trained in detecting lameness before the study commenced. A total of 318 lameness events affecting 292 cows were available for statistical analyses. For each lameness event, the lame cow’s sensor data for a time period of 14 d before observation date were randomly matched by farm and date to 10 healthy cows (i.e., cows that were not lame and had no other health event recorded for the matched time period). Sensor data relating to the 14-d time periods were used for developing univariable (using one source of sensor data) and multivariable (using multiple sources of sensor data) models. Model development involved the use of additive logistic regression by applying the LogitBoost algorithm with a regression tree as base learner. The model’s output was a probability estimate for lameness, given the sensor data collected during the 14-d time period. Models were validated using leave-one-farm-out cross-validation and, as a result of this validation, each cow in the data set (318 lame and 3,180 nonlame cows) received a probability estimate for lameness. Based on the area under the curve (AUC), results indicated that univariable models had low predictive potential, with the highest AUC values found for liveweight (AUC = 0.66), activity (AUC = 0.60), and milking order (AUC = 0.65). Combining these 3 sensors improved AUC to 0.74. Detection performance of this combined model varied between farms but it consistently and significantly outperformed univariable models across farms at a fixed specificity of 80%. Still, detection performance was not high enough to be implemented in practice on large, pasture-based dairy farms. Future research may improve performance by developing variables based on sensor data of liveweight, activity, and milking order, but that better describe changes in sensor data patterns when cows go lame.

year: 2013-11-01

title: Justifying the design and selection of literacy and thinking tools

authors: Whitehead, David

subjects: computer science

abstract: Criteria for the design and selection of literacy and thinking tools that allow educators to justify what they do are described within a wider framework of learning theory and research into best practice. Based on a meta-analysis of best practice, results from a three year project designed to evaluate the effectiveness of a secondary school literacy initiative in New Zealand, together with recent research from cognitive and neuro-psychologists, it is argued that the design and selection of literacy and thinking tools used in elementary schools should be consistent with (i) teaching focused (ii) learner focused, (iii) thought linked (iv) neurologically consistent, (v) subject specific, (vi) text linked, (vii) developmentally appropriate, and (viii) assessment linked criteria. Key words: Literacy, thinking, tools, justifying criteria.

text: Justifying the design and selection of literacy and thinking tools Criteria for the design and selection of literacy and thinking tools that allow educators to justify what they do are described within a wider framework of learning theory and research into best practice. Based on a meta-analysis of best practice, results from a three year project designed to evaluate the effectiveness of a secondary school literacy initiative in New Zealand, together with recent research from cognitive and neuro-psychologists, it is argued that the design and selection of literacy and thinking tools used in elementary schools should be consistent with (i) teaching focused (ii) learner focused, (iii) thought linked (iv) neurologically consistent, (v) subject specific, (vi) text linked, (vii) developmentally appropriate, and (viii) assessment linked criteria. Key words: Literacy, thinking, tools, justifying criteria.

year: 2008

title: Still standing, still here, still dancing.

authors: Clendon-Tau, Iwa

subjects: first person narrative

abstract: A personal narrative is presented which explores the author's experiences on teaching-learning process and her insights on language, culture and learning.

text: Still standing, still here, still dancing. A personal narrative is presented which explores the author's experiences on teaching-learning process and her insights on language, culture and learning.

year: 2002