Bibliographic record
Abstract
Multiple studies have shown that biodiversity loss can impair ecosystem processes, providing a sound basis for the general application of a precautionary approach to managing biodiversity. However, mechanistic details of species loss effects and the generality of impacts across ecosystem types are poorly understood. The functional niche is a useful conceptual tool for understanding redundancy, where the functional niche is defined as the area occupied by a species in an n‐dimensional functional space. Experiments to assess redundancy based on a single functional attribute are biased towards finding redundancy, because species are more likely to have non‐overlapping functional niches in a multi‐dimensional functional space. The effect of species loss in any particular ecosystem will depend on i) the range of function and diversity of species within a functional group, ii) the relative partitioning of variance in functional space between and within functional groups, and iii) the potential for functional compensation (degree of functional niche overlap) of the species within a functional group. Future research on functional impairment with species loss should focus on identifying which species, functional groups, and ecosystems are most vulnerable to functional impairment from species loss, so that these can be prioritized for management activities directed at maintaining ecosystem function. This will require a better understanding of how the organization of diversity into discrete functional groups differs between different communities and ecosystems.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".