The new Noah's Ark: beautiful and useful species only. Part 2. The chosen species
Bibliographic record
Abstract
For most species, conservation efforts are being determined by qualities that humans admire or dislike, including economic importance. The most universally admired physical characteristic is size: huge creatures elicit great respect, whereas the majority of species, which are small, tend to be ignored. Glamorous appearance is critical for sympathetic attention, and there are numerous features such as colour and impressive architecture that contribute to what makes a species attractive. However, bizarre or ferocious appearance, if entertaining, can also be a key to conservation. We are hard-wired to admire many of the larger mammals, provided that they have features reminiscent of health and intelligence in humans, or are ‘cute and cuddly’ like human babies. Most bird species also possess many admirable traits. However, most animals distantly related to humans, particularly invertebrates, usually have few characteristics considered attractive. The majority of the world's threatened species are insects, but except for butterflies and bees, most are usually perceived very negatively. Unfortunately, numerous animal groups in dire need of conservation, such as frogs and snakes, are decidedly handicapped by both their appearance and behaviour. The majority of species are undiscovered, and so are hardly in a position to compete for conservation attention. While there are advantages to conservation focussed on particular species, preservation of diverse habitats is preferable in order to benefit the planet's life-sustaining ecosystems and their constituent biodiversity, including humans.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.236 | 0.131 |
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".