Bibliographic record
Abstract
Known across the UK as ‘The Moat Farm Murder,’ ‘The Secret of Moat Farm’ sees Wallace as his investigative-journalist best. At the age of 28, the author was sent to cover the developments in this true-crime story. In 1903, Camille Holland and Samuel Dougal apparently went on a shopping trip. However, suspicions arose when Dougal returned alone, saying that Camille had caught a train to London. Wallace offers insights into the criminal mind, alongside all the facts that made this one of the most gripping and tragic stories of its time. A must for fans of true-crime such as Netflix\'s ´Night Stalker: The Hunt For a Serial Killer´, and those who want an introduction to Wallace’s work. Born in London, Edgar Wallace (1875 1932) was an English writer so prolific that his publisher claimed that he was responsible for a quarter of all books sold in England. Leaving school at the age of 12, Wallace made his first steps into the literary world by selling newspapers on the corner of Fleet Street. He worked as a war correspondent after joining the army at age 21, which honed his writing abilities. This led to the creation of his first book ‘The Four Just Men.’ Wallace is best remembered as the co-creator of ‘King Kong,’ which has been adapted for film 12 times (most notably directed by ‘Lord of the Rings’ director, Peter Jackson, and starring Jack Black and Naomi Watts). However, he leaves behind an extensive body of work, including stories such as ‘The Crimson Circle’ and ‘The Flying Squad’.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.102 | 0.019 |
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".