Bibliographic record
Abstract
Open Ecology is the application of biological and ecological semantic web ontologies (SWOs) and their open source secure operational development (DevSecOps) open science methodologies to the field of United Nations (UN) sustainable development goals (SDGs) participative environmental social governance (ESG) green logistics. It is being researched through my decades of internationally awarded works with some of the most respected international institutions in the fields of ecological peacemaking, welfare state policymaking, fair trade social solidarity economy (SSE), community orchards coordination, ecovilles networking, ecological literacy education, land art, garden design, ecomuseology, natural heritage safeguard ranger (Rg.), environmental diplomacy, sacred spaces greening, interfaith sacred natural sites custodianship, and synthropic agroforests management. It was compiled for the inaugural summits of the United Nations Education Science Culture Organization (UNESCO) Greening Education Partnership (GEP) and Religions for Peace (RfP) Interfaith Rainforests Initiative (IRI) 'Amazon Charter', on behalf of the Rio92 co-idealizer United Religions Initiative (URI) Movement for an Interreligious Rio de Janeiro (MIR) direction board and was vastly updated through my participation in the following United Nations Office of Sustainable Development (UNOSD) Conference of the Parties (CoPs) with the United Nations Environmental Programme (UNEP) Faith for Earth.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.181 | 0.092 |
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".