Life histories for 94 chondrichthyans used to calculate updated estimates of maximum intrinsic rate of population increase
Bibliographic record
Abstract
This dataset includes life history parameters for 94 chondrichthyans used to calculate updated estimates of maximum intrinsic rate of population increase (rmax) as shown in Pardo et al. (2016). The data here was primarily obtained from Garcia et al. (2008), however we have excluded species from their dataset that did not have values for maximum age or litter size. We have included life history parameters for the Basking Shark (Cetorhinus maximus). Life history traits included are maximum size (length_max), von Bertalanffy growth coefficient (k), age at maturity (age_mat), maximum age (age_max), average lifespan (ave_lifespan) which was defined as the midpoint between age at maturity and maximum age, litter size (l), and breeding interval (i). This dataset includes three different rmax estimates: published estimates of rmax based on the method outlined in Garcia et al. (2008) (rmax_published_previous), rmax estimates we recalculated using the method outlined in Garcia et al. (2008) (rmax_previous_recalc), and updated rmax estimates we calculated using the method outlined in Pardo et al. (2016) (rmax_updated_recalc). Given that there are slight discrepancies in rmax estimates for a few species between published values from Garcia et al. (2008) and our recalculated estimates using their methodology, we used our own rmax recalculation using the previous method to compare with our rmax estimates based on our updated method. References: García VB, Lucifora LO, Myers RA (2008) The importance of habitat and life history to extinction risk in sharks, skates, rays and chimaeras. Proceedings of the Royal Society of London, B 275: 83-89. Pardo SA, Kindsvater HK, Reynolds JD, Dulvy NK. Maximum intrinsic rate of population increase in sharks, rays, and chimaeras: the importance of survival to maturity. Canadian Journal of Fisheries and Aquatic Sciences, available at http://dx.doi.org/10.1139/cjfas-2016-0069.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".