Milk production, mortality, and economic parameters in the context of heat-stressed dairy cattle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract As climate change progresses, higher temperatures and longer periods of extreme weather are likely to increasingly impact the production and health of dairy cattle, in turn affecting farm-level profits and economic decision-making. This review identifies and summarizes the currently available research on the effect of climate-related heat stress or heat stress mitigation measures on milk yield, mortality, and economic parameters on dairy farms. A scoping review approach was adopted to map the volume, range, and characteristics of the existing body of evidence and to identify research gaps. Through a comprehensive search, 286 studies published between 2010 and 2020 were identified and underwent data extraction and analysis. These studies were conducted in 46 countries, and encompassed both research and non-research herds as well as simulation models. The Temperature-Humidity Index (THI) was the most common indicator of heat stress, although a range of atmospheric, physiological, and descriptive indicators were used. Three-quarters of these studies examined at least one heat stress mitigation strategy, such as genetic manipulations, mechanical interventions, and diet manipulation. Approximately 97% of studies evaluated the impact of heat stress on milk yield, and 10% of studies examined at least one economic parameter. Research gaps exist in the analysis of economic parameters related to heat stress in dairy cattle. Given the urgent and increasing nature of climate challenges, additional economic analyses of the effects of heat stress in dairy cattle are needed to inform production and animal health decisions in a rapidly changing environment.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it