Additional file 1 of Plankton classification with high-throughput submersible holographic microscopy and transfer learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Additional file 1: Figure S1. Left to right, distribution of taxa abundance for training set—where the distribution ratios are maintained during stratified cross validation—and the test set. Figure S2. Four classified noise objects with no resolvable features. Figure S3. Network architecture for basic CNN. Figure S4. Precision-recall curves of the InceptionV3, with iso-curves for their harmonic mean F1-score, and the area under the curve (AUC-PR). Figure S5. Precision-recall curves of the InceptionV3, with iso-curves for their harmonic mean F1-score, and the area under the curve (AUC-PR). Figure S6. Precision-recall curves of the InceptionV3, with iso-curves for their harmonic mean F1-score, and the area under the curve (AUC-PR). Figure S7. Precision-recall curves of the Xception model for each class, with iso-curves for their harmonic mean F1-score, and the area under the curve (AUC-PR). Table S1. The reference paper of four CNNs, their convolutional layers, the weighted layers that are changed during backpropagation, and broad overview of their key features. Table S2. Total time and memory expended for training and evaluating each model averaged for feature extraction and fine tuning. Table S3. Average performance of each model for each threshold metric on the test set for each fold.
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.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.920 | 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