Economic microbiology: exploring microbes as agents in economic systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microbial communities exhibit striking parallels with economic markets, resembling intricate ecosystems where microorganisms engage in resource exchange akin to human market transactions. This dynamic network of resource swapping mirrors economic trade in human markets, with microbes specializing in metabolic functions much like businesses specializing in goods and services. Cooperation and competition are central dynamics in microbial communities, with alliances forming for mutual benefit and species vying for dominance, similar to businesses seeking market share. The human microbiome, comprising trillions of microorganisms within and on our bodies, is not only a marker of socioeconomic status but also a critical factor contributing to persistent health inequalities. Social and economic factors shape the composition of the gut microbiota, impacting healthcare access and quality of life. Moreover, these microbes exert indirect influence over human decisions by affecting neurotransmitter production, influencing mood, behavior, and choices related to diet and emotions. Human activities significantly impact microbial communities, from dietary choices and antibiotic use to environmental changes, disrupting these ecosystems. Beyond their natural roles, humans harness microbial communities for various applications, manipulating their interactions and resource exchanges to achieve specific goals in fields like medicine, agriculture, and environmental science. In conclusion, the concept of microbial communities as biological markets offers valuable insights into their intricate functioning and adaptability. It underscores the profound interplay between microbial ecosystems and human health and behavior, with far-reaching implications for multiple disciplines. To paraphrase Alfred Marshall, "the Mecca of the economist lies in economic microbiology."
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it