Bibliographic record
Abstract
Underwater Eden: 365 Days features some of the most spectacular undersea photography ever taken by Jeff Rotman, who has spent over thirty years photographing life in our planet's seas and oceans. The focus is on the exquisite beauty of the world's coral reefs and inhabitants. Rotman's photographs of their vivid colors, textures and bizarre and magnificent shapes will leave readers speechless with awe. A variety of other types of ocean landscapes and ecosystems will also be featured, along with the startling diversity of wildlife found there, from sharks and dolphins to starfish and eels to anemones and coral cities. The exotic locales in which Rotman captured these images span the globe: Galapagos Islands, Red Sea, Costa Rica, Papua New Guinea, Great Barrier Reef, Vancouver Island, South Africa and Hawaii, to name but a few. Two of the richest, most diverse areas are located off the coast of Costa Rica and the Red Sea. Rotman's text will highlight scientific facts and trivia about the amazing undersea world, interspersed with interesting personal anecdotes and insight gained from his many years photographing this amazing underwater eden.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.233 | 0.084 |
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".