Bibliographic record
Abstract
Predictive modelling is fundamental to ecology and essential for objective biodiversity assessment. However, while predictive biodiversity models are generally well‐developed, models for predicting patterns within (e.g. composition, structure) and among ecosystems (e.g. ecosystem identity, spatial properties) have not been adequately operationalized. We contend the scarcity of such models marks a concerning gap in the scientific community's ability to make ecosystem predictions across landscapes, and more broadly for supporting the conservation of biodiversity and ecosystem functions. We propose ecosystem spatial pattern models (ESPM) to fill this gap in modelling capacity. Under our approach to ESPM, spatial patterns of ecosystem properties are the basis for resolving ecosystem organization at local and landscape extents. Our integrative modelling framework differs from others in that it accords biotic and abiotic constituents equally, consistent with their joint mechanistic influence on ecosystem establishment and dynamics. Development of ESPM is especially timely for ecosystem assessment is undergoing a groundswell, as scientists and conservation groups propose ambitious targets for ecosystem conservation and restoration.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.013 |
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; both teacher heads agree on what is shown here.
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".