Will Seasonality Patterns for Beef Export Sales and Commitments Hold in 2021?
Bibliographic record
Abstract
First two paragraphs: Trade occurs when price differences between the two locations are large enough after accounting for transportation cost, exchange rates, tariffs, etc. Exports vary throughout the year since prices reflect current and future supply and demand situations. Seasonality in cattle production, meat demand, and market disruptions are some examples of why wholesale beef prices increase and decrease within a year. The inability to market cattle in the second quarter of 2020 and increased demand for retail beef products due to government gathering restrictions in restaurants caused wholesale beef prices to rise to historical levels. Beef wholesalers can choose to market beef to the domestic market (retail or food service) or the export market. So how did higher domestic wholesale beef prices impact beef export sales and commitments in 2020? Likewise, knowing how the market worked through supply and demand disruptions in 2020, what can we reasonably expect from export sales and commitments in 2021? These questions can be partially answered by looking at historical seasonal export sales and commitment patterns and comparing 2020 to years with large trade disruptions.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".