On the end of evolution – Humankind and the annihilation of species
Bibliographic record
Abstract
Abstract For too long and with ever‐increasing ferocity during the past decades of an exponentially growing human world population, humankind has been waging a genuine war against nature, of which we ourselves are nevertheless also a part. We are plundering the unique biological treasure, the diversity of species, without which the ecosystems we rely on for our food, our water and so much more would not function. At present, we are already in the midst of a massive decline of populations and species, with a significantly higher extinction rate than the long‐term average. Driven essentially by habitat loss and degradation, direct exploitation through legal and illegal hunting and fishing as well as pesticides and pollution, within decades, 1 million species out of a total of 8 million could go extinct. Alongside climate change, this dramatic decay of biodiversity, that involves also the subsequent loss of ecosystem services, here termed ‘the end of evolution’, is another and by no means lesser threat to humanity. We can still take countermeasures, but we must transform our economy and change our way of living. As most successful strategy the protection of up to 30% of Earth's surface by 2030 was suggested, ideally based on the implementation of a consistent framework of global ecosystems, as it was agreed on by the December 2022 United Nations Conference of Parties (COP15) Montreal meeting for the Conservation on Biological Diversity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".