Bibliographic record
Abstract
Healthy fish populations are critical to healthy ecosystems: they feed communities, support economies and are essential to our survival. But our oceans are facing growing threats and greater uncertainty. Overfishing, climate change, habitat destruction and pollution are degrading the underwater world and putting the marine life we all depend upon at risk. Much is at stake, as the status quo is demonstrably not working. The number of stocks in the healthy zone has decreased since Oceana Canada released its 2018 Fishery Audit, and the number in the critical zone has increased — including crab and shrimp stocks. This isparticularly worrying if the depletion of crustaceans becomes a trend, as the value of Canada’s seafood industry depends heavily on them. Progress on implementing rebuilding plans remains slow and many critically depleted stocks, including northern cod, are still without a plan. As well, Fisheries and Oceans Canada (DFO) has not yet indicated how and by when it will collect adequate catch monitoring information, needed to measure and manage bycatch (the incidental catch of non-target fish) in all Canadian commercial fisheries. Meanwhile, only two of the 11 recommendations from the 2018 Fishery Audit have been implemented. DFO has made some progress since the last Fishery Audit was released. In 2019, DFO published more information to help assess fish stock health, and some elements of fishery monitoring became more transparent. DFO also implemented some of the recommendations from the 2016 Auditor General report on sustainable fisheries, including developing timelines and priorities for rebuilding plans for depleted fish populations. Most importantly, a modernized Fisheries Act became law in June 2019. For the first time in the Act’s history, rebuilding plans are now required for depleted fish populations. The government has committed more than $100 million over five years to assess and rebuild fish stocks. This brings Canada into the group of nations with modern fisheries laws and could signal a historic turning point in the health of Canadian fisheries. The impact of the new Act will depend on the strength and pace of regulations, currently under development. The regulations will outline what rebuilding plans must include, and Oceana Canada is advocating that, at a minimum, they should specify a timeline and target, aimed at rebuilding stocks to healthy levels. In the year ahead, the federal government must develop strong and effective regulations to support the rebuilding provisions in the Fisheries Act and accelerate the implementation and enforcement of existing policies. Fortunately, there is a strong base of support for new regulations to rebuild stocks, new funding commitments and much-needed increases in DFO’s science capacity to get the job done. We have the tools needed to modernize Canada’s approach to fisheries management and rebuild fish populations, and Canadians want to see this happen. In a recent Abacus Data market research survey, 98 per cent of Canadians said it was important that the federal government work to rebuild abundant fish populations. If the government fails to take these actions, we can expect the number of healthy stocks to continue to decline and depleted populations will fail to recover, impoverishing the oceans and the coastal communities who depend on them.
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 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.235 | 0.124 |
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".