Convolutional Self-Attention Neural Network for Multi-Step Forecasting of Environmental Image Series
Why this work is in the frame
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Bibliographic record
Abstract
Convolutional long short-term memory (ConvL-STM) is an enhancement of the state-of-the-art long short-term memory (LSTM) model, which has been widely used in forecasting spatiotemporal data from natural environments. ConvLSTM captures local correlations within a small receptive field through a convolutional operator, which is limited to extracting global interactions among latent variables. To further improve the prediction performance, the present paper proposes a novel attention-based ConvLSTM model for multi-step forecasting of environmental image series. Particularly, a convolutional self-attention (CSA) mechanism is developed to highlight the dependencies within hidden features during the regression and prediction processes. To validate the performance of the proposed method, experiments using a benchmark dataset and a real-world environmental dataset are conducted. The experimental results demonstrate the improved accuracy and reliability of the proposed method for multi-step forecasting of environmental image series.
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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 | 0.001 |
| 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