Early Concerns about the Environment
Bibliographic record
Abstract
Description: Historically, there had been a tendency to see random bad events, such as disasters, as “Acts of God.” These included pandemics and climate changes. The Industrial Revolution created great and growing demand for energy that was increasingly provided by “dirty” fossil fuels. It also created a strong faith in “progress.” Population growth and rising incomes contributed to the demand for more energy. The use of fossil fuels (coal, oil, gas, etc.) progressively increased the amount of greenhouse gases in the atmosphere. In recent decades, these gases have increased at a faster pace than in the past, leading, so far, to an increase in the average world temperature by more than 1 degree Celsius. The effects of this temperature change have become increasingly noticeable. The temperature is expected to increase further in future years. The increase could reach or exceed 2 degrees, which could have potentially catastrophic effects. Under present trends, such an increase seems likely by the year 2100. The medium-run effects of the temperature rise would diverge among nations, with some (especially Russia and Canada) gaining and many losing. Under current trends, it would be very difficult to stop this process, in spite of ongoing attempts to slow it down. The most recent years have been the warmest on record. There are other worrisome environmental trends that require attention and solutions (e.g., excessive use of plastic, fertilizers, and damaging chemicals).
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.066 | 0.030 |
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".