A framework to integrate and analyse industry-wide information for on-farm decision making in dairy cattle breeding /
Bibliographic record
Abstract
"The goal of this thesis was to develop a framework that could integrate and analyse industry-wide information for the support of on-farm decision-making in dairy-cattle breeding. Specific objectives included (i) describing a dairy breeding information system (DBIS); (ii) examining how the Internet could be exploited to improve the DBIS and its functioning; (iii) describing a process for implementing a unified data model to facilitate integrated user access to information in the DBIS; and (iv) developing software to support decision-making by facilitating access to a unified data model when implemented as a database management software. The first objective was achieved by following a systems approach---defining a goal, boundary, functions, structure and performance---to describe multi-organisational information systems and, specifically, a DBIS in the Canadian dairy industry. Using this framework, the subsequent analysis of the DBIS looked at its overall effectiveness. The DBIS was also compared with other known systems, where the number of participants (as well as their roles) differs from the Canadian situation. Improvements were suggested for the Canadian DBIS by focussing on the decision-maker's ability to retrieve, integrate and consider required information through information technologies. The second objective involved using the systems approach to investigate the kinds of information (if any) provided on Web sites of the DBIS participants, and to see if the Internet could be exploited to improve this process, either in terms of improved transfer speed or data transformation. It was established that the Internet is being used for rapid, flexible access to support information by DBIS participants, but that it is being under-utilised, particularly where herd output information is concerned. Herd output information could be filtered, integrated and transformed to support specific user needs at appropriate levels of intelligence density. It was further postulated that these data could be exploited more effectively through the use of such information technologies as common data exchange mechanisms and decision-support systems. The third objective was achieved through applying information engineering methods to develop a data model to represent the DBIS. This unified model was described in conceptual, logical and physical terms, and facilitated transparent access for on-farm users to information from more than one source organisation. It was demonstrated that such a model could maintain the autonomy of participating organisations while simultaneously creating an amalgamated databank for decision support. The final objective lead to the development of a prototype user interface called DAIRIE: DAiry Information Retrieval and Integration Expert which could interact with the physical schema ofthe unified data model previously developed. The interface consisted of data selection, aggregation and display forms, and allowed dynamic SQL query generation for transparent infonnation retrieval to support decision-making. Knowledge was employed in facilitating user access to information as weil as its presentation and interpretation. The approach is modular and, therefore, flexible in tenns offuture additions and improvements. The prototype shows that there is potential for creating data driven systems that cao satisfy individual uses and preferences for herd output information."@eng
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".