Pre-trained Models for SMP Classification and Segmentation
Bibliographic record
Abstract
This dataset provides access to pre-trained models that were used for SnowMicroPen profile classification and segmentation. The models were trained on a part of the MOSAiC SMP dataset, available on https://doi.pangaea.de/10.1594/PANGAEA.935554. The labeled training data consists mostly of profiles from leg three of the expedition (January - May 2020), some profiles from leg one and two, and no profiles from leg four. Please refer to the snowdragon GitHub repository (https://github.com/liellnima/snowdragon) to access the models' training code and be directed to current publications. The following trained models are available here (alphabetically ordered): Artificial neural networks Bi-directional long short-term memory (blstm.hdf5) Encoder-decoder (enc_dec.hdf5) Long short-term memory (lstm.hdf5) Baseline Majority vote classifier (baseline.model) Semi-supervised models Cluster-then-predict models: Bayesian Gaussian mixture model (gmm.model) Bayesian mixture model (bmm.model) K-means clustering (kmeans.model) Label propagation (label_spreading.model) Self-trained classifier (self_trainer.model) Supervised models Balanced random forest (rf_bal.model) Easy ensemble (easy_ensemble.model) K-nearest neighbors (knn.model) Random forest (rf.model) Support vector machines (svm.model) Loading Instructions: The models with the file-ending ".model" are pickeled Python objects and can be loaded with ``pickle.load(your_model.model)``. The random forest must be loaded with ``joblib.load(rf.model)``. All artificial neural networks are h5py.File objects (tf.keras models) and can be loaded with ``tf.keras.models.load_model(your_ann.model)``.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.027 |
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".