Scenarios are Plausible Stories about the Future, not Forecasts
Bibliographic record
Abstract
In his critique of our paper, Harron appears to have missed the intent of our work. In the spirit of the Millennium Ecosystem Assessment (http://www.maweb.org), our objective was to demonstrate how scenarios could be developed and used to help decision makers consider positive and negative implications of alternative development trajectories. Our scenarios were not intended to be forecasts or predictions, but plausible, challenging, and relevant stories about how the future might unfold given certain management strategies. We purposely parameterized the “business as usual” scenario conservatively so there would be no doubt regarding its plausibility. In the 4 years since the paper was written, the rate of development in the study area has, in fact, been significantly greater than our base case. As for the issue of avoidance of seismic lines by caribou, the avoidance effect is actually apparent up to 250 m (Dyer et al. 2001). For our modeling scenarios, we used 100 m as a reasonable cutoff for meaningful ecological impacts. This reflected the consensus estimate of 20 caribou biologists. Moreover, the projected impacts correlate well with the 50% decline in the local caribou population observed over the past decade (Alberta Woodland Recovery Team (2006), unpublished data). The most important point is that all scenarios considered in our study show striking increases in linear feature densities and any practices that lead to reduction in the size, duration, and intensity of these features will improve conditions for caribou relative to a “business as usual” scenario. This conclusion holds true regardless of the size of the area used to buffer these linear features.
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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 | 0.025 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.035 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".