Bibliographic record
Abstract
The agricultural domain is faced with the challenge of increasing amounts of collected data, multiple sources of external information and various summary reports, all tending to exist in a non-structured fashion. The formats and locations of these various data sources almost certainly differ, but profitable decision-making often depends on using and interpreting all of these inputs accurately. Based on the hypothesis that the interpretation of dairy-herd data can be aided using interactive visualization, the main goal of this research was to improve the understanding of the data-interpretation process, using such techniques, and by taking full advantage of them in the context of the Quebec dairy farm. The challenge was one of designing a system that would integrate interactive visualization techniques while also accounting for the diversity of information involved, the clientele of the dairy industry, and the technologies available. A methodological framework was developed to support the design of such an interactive visualization system, and a software prototype – HerdLine – subsequently developed. This prototype focused on the planning of a dairy-herd composition, based on the evolution of its performance over time via genetic and economic profiles. The framework helped the prototype development by better matching the characteristics of the targeted users with the data involved in the software. The result was a tool that provides an overall picture of the dairy-herd, as well as rapid, incremental, and reversible views of information contained within. An informal evaluation process was performed in order to assess the appropriateness of the prototype from a user’s point of view. Four participants, representing diverse sections of the dairy industry, downloaded, installed and tested the prototype. They subsequently completed a survey regarding their impressions to such an approach, as well as their actual experience with the prototype. Both quantitative and
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".