Anthropogenic factors affecting wildlife species status outcomes: why the fixation on pesticides?
Bibliographic record
Abstract
Most Americans are at least three generations removed from the farm, thereby at least three generations removed from the reality of where their food comes from. Not surprisingly, there are many misconceptions about modern food production, including the potential collateral environmental damage attributed to agriculture, particularly the application of pesticides. However, the implication of conventional agriculture in the broader narrative of wildlife species status outcomes (SSOs) lacks context and relativity. Since the dawn of civilization, humans have had a profound impact on their environment. Originating as hunter-gatherer societies, our ancient ancestors initially exploited anything that could be consumed or brought to bear. With the advent of the "first proto-farmers," humans began manipulating their environments to maximize available resources. Urban centers propagated and flourished proximal to agricultural origins, where modern societies have been built primarily on an abundance of food. As societies "developed," and continue to develop, an inevitable economic transition occurs from agriculture to industry/service predominance, culminating in a corresponding shift in land use. Developed countries have typically experienced maximal expansion of the agricultural frontier, where farmland is now steadily eroding by a proverbial flood of urban development. In contrast, in developing countries, this shift in economic development has not yet fully manifested and the agricultural footprint continues to expand at the expense of native habitats. Thus, the relative influence of "agriculture" on SSOs, in terms of land use, is primarily dependent on economic developmental status, which can be, at least in part, ameliorated via technology by increasing yield from existing land. Moreover, in addition to the land use challenge, there are multiple other factors affecting wildlife SSOs, including a figurative plague of invasive species, a literal plague of disease, a barrage of buildings, bumpers, grilles, and windshields to collide with, light pollution to confuse cues with, poachers to contend with, and even more complicated factors such as climate change. Being an easy target does not mean pesticides are the right target, and this fixation can potentially detract from public awareness regarding the primary drivers affecting SSOs as well as the opportunity to proactively address them. So, relatively speaking, how do these other factors compare to "pesticides" in terms of driving SSOs? Moreover, why is the popular media so fixated on the pesticide narrative? Based on the available evidence, this manuscript attempts to address these questions from a holistic and relative perspective within the context of land use change, economic development, population growth, and associated implications of global connectivity and commerce.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".