A bi-directional cross-channel RNN model for time-series forecasting of dairy production
Bibliographic record
Abstract
Predicting milk production in dairy cattle is essential for precision livestock management, a goal that can be achieved by analyzing historical cow data, including health status, milk quality, and seasonal effects. This challenge is framed as a multivariate time-series forecasting problem, requiring the effective capture of temporal dependencies and interrelationships among dairy-related variables (features). Existing time-series models often struggle to adequately represent these intricate dynamics. To address this, we propose a recurrent neural network (RNN) architecture that leverages Gated Recurrent Units (GRUs) applied bidirectionally along the channel dimension to model these interactions efficiently. The proposed model is further enhanced with feed-forward layers to implicitly capture temporal dependencies. Our approach delivers superior or competitive performance, particularly in terms of various error metrics, compared to state-of-the-art methods when predicting cumulative milk income across different lactation periods. Additionally, it exhibits relatively lower time complexity than recently proposed Transformer and convolution-based models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".