Influencia de las fases lunares en la producción agrícola
Bibliographic record
Abstract
The following engineering report titled as "Influence of the lunar phases in agricultural production" aimed to collect bibliographic information on the effect of lunar phases on agricultural production and study the influence of the moon on the planting, transplanting and harvesting of plants that they grow and fruit above and below the ground, and based on the references, knowledge was analyzed and the following conclusion was reached: Knowledge of the lunar effect in ancestral, social, and agricultural activities has pre-Columbian origins and the vast majority of Farmers believe that effectively. The Moon has direct influence on the productive activities from the point of view of agriculture, livestock and forestry. Popular beliefs and scientific research go hand by hand, on the one hand there is much scientific rigor than the other one. Most of the antecedents coincide when indicating that the sowings of the agricultural crops that grow and fructify above the ground are carried out between two to three days of the phase of the Fourth Crescent and three days after the beginning of the Full Moon. The agricultural crops that grow and fructify under the soil are made between the New Moon and the Growing Quarter phase.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".