Convolutional Self-Attention Neural Network for Multi-Step Forecasting of Environmental Image Series
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".