Bibliographic record
Abstract
The publication of the 5 th edition, a hard cover copy of this book that succeeds the 2011 paperback 4 th edition a mere four years later exemplifies the rapid advances that are occurring in this field.The previous edition of 656 pages has been expanded to 720 pages in the new edition, with changes in the number of listed authors from seven to ten, and acknowledgements to 27 reviewers.New to this edition are animations of key signaling pathways and online movies drawn from real research.Included are new experimental boxes that consider both classic and current experimental research.Most relevant to readers of this journal are "Medical Boxes" that explore developmental diseases and their genetic backgrounds that lead to congenital anomalies.Included are boxes devoted to the fast-expanding fields of preimplantation genetic diagnosis and tissue engineering using stem cells in regenerative medicine.Each chapter is richly illustrated with summaries of the contents, and end of chapter questions of both long answer nature and multiple choice format that challenges the serious reader.It is a superb pedagogical tool.Additionally, each chapter has a list of selected further readings that are remarkably up-to-date with references as recent as 2014.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.026 |
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".