The Last 400 - Strategies for Saving North Atlantic Right Whales in Canada
Bibliographic record
Abstract
Tragically, endangered North Atlantic right whales are killed each year in the watersalong the Atlantic Coast of Canada and the United States. Between 2012 and 2016,human activity killed an average of 5.6 of them every year. In recent years, more right whales are being spotted in the Gulf of St. Lawrence, likely due to the effects climate change is having on the distribution of their food source, and they have experiencedalarmingly high death rates in areas that are busy with commercial fishing activity and shipping traffic.The summer of 2017 was devastating for the population. A total of 17 North Atlantic right whale deaths were reported -12 of them in Canadian waters. The first dead whale was found on June 7 that year and by the end of the month, six had beenfound floating or washed ashore in the Gulf of St. Lawrence. History has repeated itself in 2019. From June to August, eight right whales were found dead in Canadian waters — including four within 48 hours— and four more were found entangled infishing gear.The winter started with much-needed hope and excitement with the birth of seven calves. But given that not every carcass is found, and that the death toll has already exceeded the number of known births, 2019 is yet another year of decline for right whales. What’s more, four of the eight deaths were reproductively active females, of which there are fewer than 100 left. On the surface, these numbers might not seem like much.However, North Atlantic right whales are among the most endangered species on the planet, and 2017 and 2019 have dealt catastrophic blows these animals could have done without. Given that only about 400 of them remain,10 the loss of 28 right whales over the last three years (17 in 2017, three in 2018 and eight in 2019) amounts to seven per cent of the species’ population.The North Atlantic right whale population is teetering on the brink of extinction. Many of the few remaining animals are dying horrible deaths as a result of ship strikes and entanglements in fishing gear. The whales that manage tosurvive injury are often left weak and vulnerable. Scientists have long known, and recent research confirms, that humans continue to cause a high rate of right whale deaths.Every single death deepens the urgency with which we must act to stop the tragedy unfolding in the Atlantic. Many people are working hard to save right whales, but more must be done.The Canadian government must do everything possible to halt this disastrous downturn. If nothing changes, we could witness the extinction of this species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".