Bibliographic record
Abstract
Our definition of the word 'animal' centers on vertebrates, yet 99% of the animals on the planet are invertebrates, about which we know little. In addition, although the Census of Marine Life (COML.org) has recently conducted an extensive audit of marine ecosystems, we still do not understand much about the animals of the seas. Surveys of the best-known ecosystems, in which invertebrate populations often play a key role, show that the invertebrate populations are affected by human impact. Coral animals are the foundation of coral reef systems, which are estimated to contain 30% of the species in the ocean. Physical impact and chemical changes on the water severely damage these reefs, and may lead to the removal of these important habitats. Tiny pteropod molluscs live in huge numbers in the polar seas, and their fragile shells are particularly vulnerable to ocean acidification. Their removal would mean that fishes on which we depend would have a hugely diminished food supply. In the North Sea, warming is leading to replacement of colder water copepods by warmer water species which contain less fat. This is having an effect on the birds which eat them, who enrich the otherwise poor land on which they nest. Conversely, the warming of the water and the loss of top predators such as whales and sharks has led to an explosion of the jumbo squid of the Pacific coast of North America. This is positive in the development of a squid fishery, yet negative because the squid eat fish that have been the mainstay of the fishery along that coast. These examples show how invertebrates are key in the oceans, and what might happen when global changes impact them.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".