The Essential Guide to the Loch Ness Monster and Other Aquatic Cryptids by Ken Gerhard
Bibliographic record
Abstract
This is a readable overview of reports of unidentified aquatic creatures in the oceans and in freshwater, and it can be a useful introduction for people who have not previously read much about this subject. Cryptozoologists, however, will find nothing new here; the treatment is purely descriptive rather than analytical and critical. A nice Foreword by Steve Feltham, in residence at Loch Ness for 30 years looking for Nessies, includes the important point that the number of actual sightings is a large multiple of the number of publicly known reports. The first two chapters are about the Loch Ness Monster. Chapter 3 deals with sea serpents. Chapters 4 through 6 are about the Canadian “Ogopogo” of Lake Okanogan, the American “Champ” of Lake Champlain, and less-well-known lake monsters of North America. Chapter 7 reports on lake monsters around the world. Chapter 8 surveys the typically mysterious carcasses periodically found on seashores. Chapter 9 mentions the surprises that the ocean depths occasionally reveal, notably the coelacanth, the giant squid, and the megamouth shark, as well as the little-known beaked whales, oarfish, and sturgeon of monstrous size.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.094 | 0.074 |
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".