Bibliographic record
Abstract
DARREL FALK Dr. Falk received his B.Sc. from Simon Fraser University, and earned his Ph.D. from the University of Alberta. He did postdoctoral work at The University of British Columbia and the University of California, Irvine before accepting a faculty position at Syracuse University in New York. In 1988 he transitioned into Christian higher education in the biology department at Point Loma Nazarene University in San Diego, where he is now Emeritus Professor of Biology. He is a member of the American Association for the Advancement of Science, the Genetics Society of America, and the American Scientific Affiliation. He is the author of Coming to Peace with Science: Bridging the Worlds Between Faith and Biology, and he speaks frequently on the relationship between science and faith at universities and seminaries. TODD WOOD Dr. Wood is the Core Academy president and professor of biochemistry. He is a graduate of Liberty University and the University of Virginia. He worked for thirteen years at Bryan College before starting Core Academy of Science. Todd authored or co-authored more than 40 technical papers, and he currently serves as president of the Creation Biology Society. Todd was featured in a 2012 cover article in Christianity Today. He speaks to churches and schools on issues related to science and faith, creationism, and creationist biology.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.668 | 0.371 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".