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Record W4382751123 · doi:10.3390/su151310350

Undergraduate Internship on Yamuna River Exploration: Raising Awareness about SDG6

2023· article· en· W4382751123 on OpenAlexaff
Sunita Gupta, Parul Kulshreshtha, Divya Aggarwal, Deuvshree Sharma

Bibliographic record

VenueSustainability · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersDepartment of Biotechnology, Ministry of Science and Technology, India
KeywordsInternshipField tripCurriculumEcologySociologyPedagogyMedical educationPolitical scienceMedicineBiology

Abstract

fetched live from OpenAlex

A semester-long internship was designed for undergraduate students at the University of Delhi, India. Teams comprising 30 students from all over the University were trained to carry out field exploration activities on the Yamuna River flowing in Delhi. The students were provided with portable kits to measure the physicochemical parameters at a selected bank of the Yamuna River. Students documented the invertebrate fauna in the riparian zone of the banks. At the same time, they noted the anthropogenic polluting activities on the bank of the Yamuna River. This internship educated students about the UN’s Sustainable Development Goals (SDGs). They all studied Ecology and Environmental Science in their undergraduate curriculum, but they all confessed that they were unaware of the 17 SDGs and the deteriorating health of the Yamuna River in the city. We educated students about the freshwater emergency recovery plan and the International Union for Conservation of Nature (IUCN) red list of ecosystems. This internship is a great example of an undergraduate-directed study or research experience that supported student constructivism and inquiry-based learning, and this research article elaborates on student reports and situational interest in freshwater biology to achieve SDG6.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.072
GPT teacher head0.336
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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