Data for: Global change risks a threatened species due to alteration of predator-prey dynamics
Bibliographic record
Abstract
Datasets generated and analyzed within the study area located in the Côte-Nord region of Québec, Canada. To identify species-specific movement rules that were implemented in the IBM, we used empirical data collected for caribou, moose, and wolves over the study area. "DataFinal_SSF_Species_season.csv" (6 files) were used to develop Step Selection Functions for caribou, moose, and wolves to assess habitat selection. SSFs were estimated from data for the real animals and provide the relative probability of selection among a set of options based on the comparison of observed and random steps (i.e., the linear segment between successive locations at 8-h interval) using conditional logistic regression (Fortin et al. 2005). Details on GPS data and SSF models can be found in the article in Appendix S1: Section S2. SSFs compare resource characteristics of observed (scored 1) and random (scored 0) locations presented in column case. Habitat characteristics (columns conif_dense, conif_open, mixed, open, other, fire010, fire1020, fire20, cut010, cut1020, cut20) was extracted from the Canadian National Forest Inventory (NFI) forest cover maps. Land cover maps were updated every year by adding roads, recent (<5 years), regenerating (6–20 years) and old (21–50 years) cutblocks/fires based on information provided annually by local forestry companies and from the Canadian National Fire Database (CNFDB). Columns dist0_0.25, dist0.25_0.50, dist0.5_1.00, dist1.00_1.5, and dist1.5 are a set of 5 dichotomous covariables representing the classes of distance to the nearest road (i.e., 1) ≤250 m, 2) 251–500 m, 3) 501–1000 m, 4) 1001–1500 m and 5) >1500 m as the reference category). "DataFinal_IBM_Caribou_Season.csv" (2 files) corresponded to the IBM outputs with the proportion of caribou agent killed (Prop.Caribou_killed, number of caribou killed/total number of caribou), in function of the different scenarios (CC,LUC,Year,Season,Scenario) and the response (Behavioral-Numerical responses or Behavioral response). The columns Prop.CutsRoads, Prop.Fire, Prop.Broadleaf, Homogenization, Isolation correspond to the different variable we tested to predict the cumulative impact of anthropogenic disturbance and climate change. To explore how changes in forest structure and composition impacted the proportion of caribou killed, we used the proportion of areas disturbed by cuts and roads (Prop.CutsRoads), burned areas (Prop.Fire), and landscape characteristics, such as the proportion of deciduous vegetation (Prop.Broadleaf), landscape homogenization (Homogenization) and isolation (Isolation) of mature conifer stands.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.019 |
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".