Improved probability of detection of ecological “surprises”
Bibliographic record
Abstract
Ecological "surprises" are defined as unexpected findings about the natural environment. They are critically important in ecology because they are catalysts for questioning and reformulating views of the natural world, help shape assessments of the veracity of a priori predictions about ecological trends and phenomena, and underpin questioning of effectiveness of resource management. Despite the importance of ecological surprises, major gaps in understanding remain about how studies might be done differently or done better to improve the ability to identify them. We outline the kinds of ecological surprises that have arisen from long-term research programs that we lead in markedly different ecosystems around the world. Based on these case studies, we identify important lessons to guide both existing studies and new investigations to detect ecological surprises more readily, better anticipate unusual ecological phenomena, and take proactive steps to plan for and alleviate "undesirable" ecological surprises. Some of these lessons include: (i) maintain existing, and instigate new, long-term studies; (ii) conduct a range of kinds of parallel and concurrent research in a given target area; (iii) better use past literature and conceptual models of the target ecosystem in posing good questions and developing hypotheses and alternative hypotheses; and (iv) increase the capacity for ecological research to take advantage of opportunities arising from major natural disturbances. We argue that the increased anticipatory capability resulting from these lessons is critical given that ecological surprises may become more prevalent because of climate change and multiple and interacting environmental stressors.
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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 | 0.035 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".