Disruption of an ant-plant mutualism shapes interactions between lions and their primary prey
Bibliographic record
Abstract
Data and file overview: Kamaru_Path_Analysis_Data.csv Kamaru_Path_Analysis.R Kamaru_Zebra_RSF_Data.csv Kamaru_Zebra_RSF.R Layers used to build Zebra RSF: Kamaru_DWater: distance to water Kamaru_DGlade: distance to glade Kamaru_DSettlement: distance to human settlement Kamaru_OPC_Veg: vegetation layer (classes: V. drepanolobium, E. divinorum, others) SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis_Data.csv Number of variables: 11 Description: This data file includes 105 zebra kill sites and paired random locations from June 2019 to August 2020. It also includes: (A) monthly utilization distributions of lion prides associated with each kill site and paired point; and (B) zebra densities estimated from resource selection functions, associated with each kill site, and paired random location. Please see our supplementary materials for more details on data and methods. Variable list: (A) rsf.block: Resource Selection Function blocks (block 1: Jan-Apr 2019, block 2: May-Sep 2019, block 3: Oct 2019 – Jan 2020, block 4: Feb-May 2020, block 5: Jun-Sep 2020) (B) Kill_ID: kill identifier. (C) Lion_ID: individual lion pride identifier. (D) Date (Day, Month, Year) when a specific kill occurred. (E) Zebra_kill (1 = kill site, 0 = paired random location). (F). Species: Zebra. (G) Visibility: openness measurement using a rangefinder in (m). (H) Lion_activity: Utilization distributions (UD) of lions. (I) Invasion (1 = invaded by big-headed ants, 0 = uninvaded by big-headed ants). (J) zeb.rsf: resource selection function value. (K) zeb.density: zebra density estimated from resource selection functions. SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF_Data.csv Number of variables: 10 Description: This data file includes 182 zebra sightings, paired with 10 random points created for each sighting/used point. Also, the data includes actual GPS locations of each sighting and the total number of zebras in each sighting. Please see our supplementary materials for more details on data and methods. Variable list: (A) Species: Zebra. (B) Date (Day, Month, Year) for that sighting. (C) Survey: count identifier (Survey 2 to 21). (D) GPS location (X and Y), longitude and latitude of that sighting location. (E) Transect: Transect number. (F) Used: (1= zebra sighting, 0 = paired point). (G) zebra.ct: total number of zebras in each sighting. R CODE SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis.R Description: Apply this code to Kamaru_Path_Analysis_Data.csv to build nested path models. SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF.R Description: Apply this code to Kamaru_Zebra_RSF_Data.csv to build resource selection functions for zebra. Use the following layers: Kamaru_DWater, Kamaru_DGlade, Kamaru_DSettlement and Kamaru_OPC_Veg to build the Zebra RSF.
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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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".