Stacked Bidirectional LSTM for Predicting Emission of Nitrous Oxide
Bibliographic record
Abstract
In this paper, a stacked Bidirectional Long Short-Term Memory (BDLSTM) is proposed to predict the emission of Nitrous Oxide (N 2 O) from agricultural soils.The model is trained using the data collected by LI-COR soil-gas measurement equipment in Area X.O.Ottawa, Canada.With MSE as the loss function and Adam as the optimizer, the model is evaluated against mean absolute error(MAE), mean absolute percentage error (MAPE), and root mean square error(RMSE).In comparison with the MLP model, it is observed that the stacked BDLSTM model has superior performance.The model is trained on two thousand data points with an early stopping technique, which, in general, is used to avoid overfitting in a highly complex model.Although the overfitting is depressed with the early stopping technique, it is necessary to consider the trade-off between the computation efficiency and the prediction accuracy.In this study, two BDLSTM layers (each with 100 hidden nodes) would have achieved the optimal balance.Also, the experiment shows that if an unrelated feature is added to the input features, it will degrade the performance of the model, and the simulation also indicated that it is necessary to choose an appropriate number of time steps (12 time steps) to obtain the best prediction accuracy.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".