Szumski et.al. 2023. Ecosphere. Canada lynx foraging strategies: facultative specialists become obligate generalists towards distribution edge.
Bibliographic record
Abstract
Data, analysis script, and results for: Szumski, C.M., Roth, J.D., and Murray, D.L. 2023. Canada lynx foraging strategies: facultative specialists become obligate generalists towards distribution edge. Ecosphere (in press). R script for all analyses: Code_Lynx_SpatioTemporalAnalysis.txt Snowshoe hare geolocation and stable isotope data: Table_SnowshoeHare.csv Canada lynx geolocation and stable isotope data: Table_ST_Lynx_AK.csv Table_ST_Lynx_MB.csv Table_ST_Lynx_QC.csv Table_ST_Lynx_YT.csv Trophic enrichment factors used in proportional diet reconstruction analyses: SpatioTemp_Discrim_Neilson_Miller_and_Parng.csv Raw stable isosope data (i.e., not corrected for trophic enrichment) of prey species (snowshoe hare, red squirrel) subset by study region: Table_ST_Prey_Raw_AK.csv Table_ST_Prey_Raw_MB.csv Table_ST_Prey_Raw_QC.csv Table_ST_Prey_Raw_YT.csv Posterior distributions for the proportion of snowshoe hare in lynx diets reported in the manuscript: AK_pfac1.csv MB_pfac1.csv QC_3yr_pfac1.csv YT_pfac1.csv Lynx stable isotope data subset for SIBER analyses: Table_ST_SIBER_AK.csv Table_ST_SIBER_MB.csv Table_ST_SIBER_ON.csv Table_ST_SIBER_QC.csv Table_ST_SIBER_YT.csv
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.093 | 0.034 |
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".