Citizen Science, Fall/Winter 2016, Issue 33
Bibliographic record
Abstract
A student collecting samples for a citizen driven research project.Citizen science over the past decade has grown exponentially and has become a tool for researchers whose studies require large amounts of data, data collection over large geographic areas, data that needs to be interpreted and formatted to allow study, or where monitoring and data collection costs are beyond available financial resources.The term 'citizen science' refers to public participation in organized research efforts around scientific topics.Individuals participating in citizen science are not necessarily trained in the sciences.Acronyms for citizen science include "crowdsourced science," "open science," "volunteer monitoring," "grassroots", or "lay science."The key characteristic that defines citizen science is that it be based and guided by scientific research principles.The concept is simple: use the power of many to gather data on a scale that no single scientist could gather in a lifetime.A citizen science project can involve a few or millions of people collaborating towards a common goal.In a typical project, a research question is identified, methods of data collection determined, volunteers sign up to collect data, volunteers may receive some training, data is collected, and then the data is analyzed by scientists and the participants themselves.This contributory model has citizens collect and submit data under the guidance of a science researcher or advisory group.The environmental/sustainability fields that citizen science advances are diverse: ecology, biology, hydrology and water quality, astronomy, public health, computer science, statistics, geography, meteorology, engineering and many more.There are almost 1000 projects listed within the 'Zooniverse', the home of Citizen Science on the web.Scientific American magazine lists over 200 citizen scientist programs (www.scientificamerican. com/citizen-science), www.SciStarter.com,catalogs over 600 citizen science projects, but there are more likely thousands of citizen science programs and studies going on nationally.The scope of topics being addressed by citizen scientists is boundless, but generally fall into a few categories: monitoring, inventory, assessment, discovery, trend analysis, mapping and interpretation.Table 1 provides examples of existing citizen science projects currently underway.Monitoring the environment includes measuring the quality of air, water, soil, biodiversity, and habitats.Long a public agency responsibility, with budget stagnation and cuts, the ability to measure the quality of our environment is limited by a shrinking number of monitors who can be supported.Citizen science has been successfully implemented to conduct inventories of flora and fauna to allow scientists and land managers to understand the geographic scope and populations of specific species.Since 1980, amphibians have dramatically declined in populations with 32% of the world's amphibian species now classified as threatened.Habitat loss, climate change, pollution, introduced species, destruction of the ozone layer all may have contributed.Citizen scientists conducting inventories provide a way to understand the status of amphibians and what can be done to protect them.Nationwide FrogWatch programs exist in the US and Canada where citizen scientists are reporting observations of frogs.Assessments are the evaluation or estimation of the nature, quality, or ability of something to survive.Season Spotter is asking volunteers to help identify changes in plants, shrubs, and trees over the seasons, to better understand and assess the impact of climate change on vegetation.Citizen scientists are working to discover new celestial objects.Citizen science, by monitoring, inventorying, assessing and discovering, can begin to measure trends and changes in the environment.In many ways, measuring trends may be one of the most valuable outcomes of citizen science.With a wide variety of participants with diverse skills, citizen science projects are able to map and interpret data being collected in ways to best communicate issues of concern, the significance of the problem, and optimal solutions.The size of citizen science projects may range from one person to millions of people.In the winter of 1881-82, Wells Cooke, a member of the American Ornithologists' Union, asked for bird watchers in Iowa to send him lists of winter bird residents and the dates of the first arrivals of spring migrants.The data collected from this citizen science effort led to a long-term study of bird migration in the Mississippi River corridor.The project was started through the efforts of one person.With more inclusive communication systems and ability to store and analyze
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".