One hundred years of population ecology: Successes, failures and the road ahead
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Population ecology is the most mature of the three subdisciplines of ecology partly because it has a solid mathematical foundation and partly because it can address the primary questions of distribution and abundance with experimental protocols. Yet there is much left to do to integrate our population knowledge into community and ecosystem ecology to help address the global issues of food security and the conservation of biodiversity. Many different approaches are now being developed to bring about this integration and much more research will be necessary to decide which if any will be most useful in achieving our goals of explaining the changes we see in the distribution and abundance of animals and plants. Food web ecology would appear to be the best approach at present because it uses the detailed information of the population ecology of particular species in combination with data on consumer-resource interactions to apply to the applied problems of biodiversity conservation, food security, pest management and disease prevention. If we can use our understanding of population ecology to address the practical problems of our time in a creative way, we will benefit both the human population and the Earth's biodiversity. Much remains to be done.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it