Advancing terrestrial ecology by improving cross-temporal research and collaboration
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
Ecology spans spatial and temporal scales and is inclusive of the history of life on Earth. However, research that occurs at millennial timescales or longer has historically been defined as paleoecology and has not always been well integrated with modern (neo-) ecology. This bifurcation has been previously highlighted, with calls for improved engagement among the subdisciplines, but their priority research areas have not been directly compared. To characterize the research agendas for terrestrial ecological research across different temporal scales, we compared two previous studies, Sutherland and colleagues (2013; neoecology) and Seddon and colleagues (2014; paleoecology), that outlined priority research questions. We identified several themes with potential for temporal integration and explored case studies that highlight cross-temporal collaboration. Finally, a path forward is outlined, focusing on education and training, research infrastructure, and collaboration. Our aim is to improve our understanding of biodiversity patterns and processes by promoting an inclusive and integrative approach that treats time as a foundational concept in ecology.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it