Reconstructing historical abundances of exploited marine mammals at the global scale
Bibliographic record
Abstract
The relationship between humans and marine mammals extends back for centuries and covers a multitude of facets; literature and spirituality, necessities (food) and luxuries (corsets). Their exploitation history changed from small-scale hunting for foodlfur, to large commercial ventures for oil, to limited hunting as marine mammals became the poster child for environmental movements. The International Whaling Commission, national and regional bodies assess the current status of marine mammals. Information on stock size relative to carrying capacities is, however, hard to come by. Assessments to date have been limited to a relatively small number of stocks/species because data quality, availability and politics. To address concerns over stock status, the analyses have to be expanded to all exploited populations. Employing a Bayesian approach to stochastic stock reduction analysis, I construct probability distributions over historical stock sizes. The method allows me to use historical catch time series to estimate a distribution over population parameters, the intrinsic rate of growth and carrying capacity, that give rise to extant populations. I tested this stock reconstruction approach on simulated data sets generated from a reference model, and then applied it to available catch and abundance data. Simulation tests indicate that parameter estimates were unbiased. Globally, I find that aggregated information on all marine mammal populations indicate a decline of 22% (0-62%) in numbers, and 76% (58-86%) in biomass. The decline has been greatest for the great whales, with a 64% (40-79%) decline in numbers, and an 81% (69-89%) decline in biomass. My estimates are consistent with published estimates of carrying capacity. These estimates are intended to provide a status overview, rather than direct management advice, affording us the ability to begin calculating how humans have impacted these large fauna in their marine ecosystems. The International Convention on the Regulation of Whaling (ICRW) predicates that stocks must be maintained at their most productive levels. Also, a moratorium on whaling exists, awaiting better science. This represents the fundamental conflict in interest between pro-whaling nations and those with morallethical opposition to whaling, intent on upholding the moratorium. Results in this thesis indicate that the Japanese hunt of minke whales could increase and abide by the ICRW, yet the majority of western IWC signatories want to see commercial hunting banned altogether.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".