Going further with model verification and deep learning
Bibliographic record
Abstract
Abstract In our recent review paper aiming to introduce deep learning to ecologists, we presented a workflow describing the steps required to create a deep learning model. This figure did not present some of the following steps of model use such as model verification. By ensuring model adequacy, model verification is an important step after model creation in order to answer ecological questions. Adding model verification to a deep learning model development workflow can raise some new issues such as detecting the difference among the multiple datasets or what to do when model verification fails. In the spirit of our previous review, we identify some questions users trying to verify their deep learning model can have and try to find, for each, a solution to help them navigate the steps of deep learning model testing. We provide an additional cheat sheet to quickly help answer common questions regarding using model verification and deep learning. We hope these resources help stimulate further synthesis and coherence in the use of deep learning models in ecology.
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 | 0.039 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".