Bibliographic record
Abstract
This Paper is directed at the millions of PSU miners. Metalworkers and their families and particularly their children and support staff @ education institutions-school levels, college and higher studies. Food shortages, extreme temperatures and SCM disorders have begun arising out of and in the course of water shortages with top soil erosions and human induced climate changes. Sadhguru Jaggi Vasudeva, is already on a 100-day motorcycle journey from Europe to India via Middle East, to raise public awareness1. It is estimated by 2045, top soils across the world will have degenerated beyond productivity. States and Institutions must take corrective actions. Corrective behavioural changes are much necessary. Are we drifting under false aspirations induced by the charms of Smartphone and Information Highways beyond redemption? The State of the Indian Union can indicate the emerging crisis up to a point. The Media instead of highlighting entertainment issues via TV serials/Musical Events for commercial advantages, must take a proactive role. Suddenly indescribable Disasters will hit us across coastal, mountainous and deep mineral interiors. Are we ready? Activated minds of the leaders of the PSU/ Government Sectors can revive the semi-dormant minds of their constituents. Given global field-tested research, statistical surveys and analysis thereof by natural scientists’ world over confirm the disintegration of Mother Earth has begun. Something is frightfully wrong with human prioritizations in 21st Century-@ micro family/community/ small town levels and macro government policy executions! Human Race is casual & not causal (cause & effect) oriented. Actually, as you read this Paper the beginning of the 6th Mass Extinction has begun. This Geominetec Conference and other avenues are worthy opportunities to share concerns for our future generations through the intelligent minds of leaders who matter. Yes, our leaders must rise to the occasion now.
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 | 0.002 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".