Bibliographic record
Abstract
The demand for dairy goat products like cheese, fluid milk, ice cream, and keifer has increased in the last decade. There has been an increase in number of producers raising goats with little prior goat experience as well as investment of large cheese processors into the market (Saputo, Emmi, and Feihe). Notably, national herd sizes are growing. In 2020, the total number of goats in the US was 2.66 million which included 440,000 dairy goats. Canada had a 79% increase in goat numbers from 2001 to 2016. Despite this growth, a very limited number of evidence-based resources are available for North American producers to educate themselves on dairy goat wellbeing. The goals of this project was to provide the dairy goat industry with education and training materials on dairy goat wellbeing. Four topics were prioritized based on their importance to the industry including disbudding, euthanasia, hoof trimming, and transportation. @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:3 0 0 0 1 0;}@font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4; mso-font-charset:0; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-1610611985 1073750139 0 0 159 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0in; text-indent:.25in; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}h1 {mso-style-priority:9; mso-style-unhide:no; mso-style-qformat:yes; mso-style-link:"Heading 1 Char"; mso-style-next:Normal; margin-top:30.0pt; margin-right:0in; margin-bottom:4.0pt; margin-left:0in; mso-pagination:widow-orphan; mso-outline-level:1; border:none; mso-border-bottom-alt:solid #2F5496 1.5pt; mso-border-bottom-themecolor:accent1; mso-border-bottom-themeshade:191; padding:0in; mso-padding-alt:0in 0in 1.0pt 0in; font-size:12.0pt; font-family:"Calibri Light",sans-serif; mso-ascii-font-family:"Calibri Light"; mso-ascii-theme-font:major-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:major-fareast; mso-hansi-font-family:"Calibri Light"; mso-hansi-theme-font:major-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:major-bidi; color:#2F5496; mso-themecolor:accent1; mso-themeshade:191; mso-font-kerning:0pt;}span.Heading1Char {mso-style-name:"Heading 1 Char"; mso-style-priority:9; mso-style-unhide:no; mso-style-locked:yes; mso-style-link:"Heading 1"; mso-ansi-font-size:12.0pt; mso-bidi-font-size:12.0pt; font-family:"Calibri Light",sans-serif; mso-ascii-font-family:"Calibri Light"; mso-ascii-theme-font:major-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:major-fareast; mso-hansi-font-family:"Calibri Light"; mso-hansi-theme-font:major-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:major-bidi; color:#2F5496; mso-themecolor:accent1; mso-themeshade:191; font-weight:bold;}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:11.0pt; mso-ansi-font-size:11.0pt; mso-bidi-font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}.MsoPapDefault {mso-style-type:export-only; text-indent:.25in;}div.WordSection1 {page:WordSection1;}
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.367 | 0.088 |
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".