Living in a global environmental emergency ward – the need to address problems with science, action and speed
Bibliographic record
Abstract
EDITORIAL Living in a global environmental emergency ward -the need to address problems with science, action and speedWelcome to the new Issue of the PNSIS.This piece is being written at the end of the summer, midway through another hurricane season in the Maritimes and this one purported to be noteworthy.So far, three storms have barreled up the Atlantic coast, with post-tropical storm Lee hitting us directly and with more predicted to come.This follows several months of unsettled weather, resulting in Nova Scotia being marred by wildfires, excessive rain, unusual and tragic flooding, and a slow recovery from last years, massive subtropical storm Fiona.Wildfires continue in Quebec and out west in Alberta, BC and the NWT, causing people to evacuate and resulting in enormous loss of forests, homes and other community infrastructure.It has made Canadians across the country, especially residents of Nova Scotia, much more aware of extreme weather events and the changing climate, and wondering what the future holds for their descendants.During times of reflection about such issues, the environment in general, and with my head in an excellent autobiography penned by a distinguished environmental biologist (Ehrlich 2023), it appears as though we are currently living in an environmental emergency ward, the patients (forests, waters, coastal and ocean ecosystems, wildlife, our homes and communities, us!) coming in faster than they can be diagnosed, stabilized and treated.Although the gravity of these events may be accentuated by the 24 hour news cycle, it is clear that the problems we face are real, serious and a bit overwhelming.Throughout much of my career as a marine environmental scientist, I have felt that based on the facts, humanity is in a dire environmental situation.This was predicted by some but until recently, not generally recognized.My first wake-up call to the challenges was as a young zoology graduate student involved in a global population conference in Fall,1968 (Regier and Falls 1969).It alerted me to the population explosion that was occurring throughout the 20th century (3.45 billion in 1968, now 8 billion in 2022), with many implications for society.Soon afterwards, I read the literature on global issues of concern
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.056 | 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".