2040: Climate Change Documentary Analysis
Bibliographic record
Abstract
This paper examines the impacts of climate change on human health and well-being, utilizing insights from the documentary 2040, directed by David Gameau. It explores how agriculture and ocean ecosystems can contribute to climate change mitigation. While the agricultural industry is a significant source of emissions, it is also particularly vulnerable to climate impacts (Gameau, 2019). Rising ocean temperatures and acidification threaten biodiversity and disrupt vital ocean circulation (Gameau, 2019). This paper highlights the interrelation of climate change, planetary health, and human well-being and advocates for a multisectoral approach. It emphasizes strategies like sustainable agriculture and marine permaculture alongside adaptation measures to reduce vulnerability. This research accentuates the importance of environmental justice and the need for equitable and inclusive climate action. Through examination of the United Nations Sustainable Development Goals (SDGs), such as zero hunger and responsible consumption, this paper identifies crucial intervention areas. Recommendations include reducing waste, promoting sustainable consumption, and implementing upstream policies to support climate mitigation. The research highlights the urgent need for global action to combat climate change and protect human health for future generations. Given the climate crisis's implications for nursing practice, adopting planetary nursing approaches is essential to safeguard both the planet and its population.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".