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Introduction

2018· book-chapter· en· W4252863246 on OpenAlexaboutno aff
Jason M. Colby

Bibliographic record

VenueOxford University Press eBooks · 2018
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryCorporationGeographyFisheryPolitical scienceLawBiology

Abstract

fetched live from OpenAlex

As a boy, I saw my dad cry on only three occasions. One was his father’s funeral. The other two involved dead orcas. In the 1970s, he worked as curator of Sealand of the Pacific, a small oceanarium near Victoria, British Columbia, and then for the Seattle Marine Aquarium and Sea World. On both sides of the US-Canadian border, across the Salish Sea, he helped capture killer whales for sale and display—or, as he darkly joked, “for fun and profit.” Tell someone today that your father caught orcas for a living and you might as well declare him a slave trader. Killer whales are arguably the most recognized and beloved wild species on the planet. They are certainly the most profitable display animals in history, and with the 2013 release of Blackfish, their fate became an international cause célèbre. Broadcast and distributed by CNN, the film became one of the most influential documentaries of all time. Already years into my research for this book when the movie came out, I found little in it surprising. But Blackfish turned my father, long conflicted about his past, sharply against orca captivity. He wasn’t alone. Almost over­night, viewers, politicians, and activists turned their sights on Sea World—a multibillion-dollar corporation famous for its killer whale shows. In this debate, it seemed there was no room for nuance or history. Millions around the world simply knew in their hearts that orcas had to be saved from captiv­ity. What they didn’t realize was that, decades earlier, captivity may have saved the world’s orcas. Orcinus orca is the apex predator of the ocean, but that ocean has changed rapidly in recent decades. Following World War II, rising populations and new technology drove humans to plunder the sea as never before, and many regarded killer whales as dangerous pests. By the 1950s, whalers, scientists, and fishermen around the world were killing hundreds, perhaps thousands, per year. In a single expedition, celebrated by Time magazine, US soldiers slaughtered more than one hundred off Iceland. But then a curious thing happened.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.443
Threshold uncertainty score0.631

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0070.005
Open science0.0020.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.5570.415

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.

Opus teacher head0.017
GPT teacher head0.193
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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