Turn on the lights! Fundamental science leads to scientific progress: A perspective from developmental biology
Bibliographic record
Abstract
Many of the ways in which we interact with the world around us have been shaped by the dual efforts of fundamental and applied sciences. Generally speaking, fundamental science generates knowledge about how things work at a fundamental level, and applied science employs this body of knowledge to create a new product or overcome an existing challenge. Developmental biology is a classic example of fundamental science that drives several avenues of applied science. For example, understanding how cells, tissues, and organs develop, and are coordinated within a functioning organism can form the basis for diagnosing medical conditions and exploring treatments. As a developmental biology lab researching bone and cartilage in birds and fish, we are acutely aware of the disparity in financial support between fundamental and applied science research in Canada. Funding cutting-edge applied research with immediate impacts on society is attractive and more easily justifiable to tax payers. However, funding grassroots fundamental science research is equally important but receives significantly less attention and support because the impacts are harder to predict and are longer-term. This commentary addresses this inequity in science funding and high-lights the dire need to improve supports for early career scientists.
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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.029 | 0.044 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".