Dairy Goat Research Facility: Revealing Process & Provoking Interactions
Bibliographic record
Abstract
The dairy goat industry in Ontario is in need of benchmarking data on the raising \nand caring for goats resulting in better welfare and production. The power of collective \ndata cannot be overstated to benefit and advance the industry as seen in Ontario’s dairy, \nswine and poultry industries. The goal of this thesis is to create a framework for the design \nbehind a research facility dedicated to dairy goats with the animals as the main priority. \nAs a dairy goat farmer and an architecture graduate student, I believe my experiences \nand knowledge in agriculture and architecture have given me the tools to understand the \nparticularities of goats and how their environment may affect them. The research studies \non goat behaviour and welfare analyzed in this thesis, encompass a range of aspects from \nunderstanding social needs, and evaluating adaptable behaviour, to assessing the performance \ncharacteristics of materials and housing components. The emerging designs for the Dairy Goat \nResearch Facility embody an integration of goat behaviour research, farm culture and \nindustrialization, and sustainability.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".