Bibliographic record
Abstract
FOLIJARNA ISHRANA VINOVE LOZEIshrana biljaka -gnojenje, jedna je od najvažnijih stvari u tehnologiji proizvodnje svake kulture.Vinova loza je skromna biljka i traži za rast i razvoj manje koliËine hranjiva nego ostale kulture, no i uz tu skromnost potrebuje mnoge makro i mikro elemente (dušik, fosfor, kalij, kalcij, magnezij, sumpor, bor, bakar, željezo, mangan, cink, molibden).Ako neki elemenat nedostaje, smanjuje se i kvaliteta i koliËina prinosa.Prije dodavanja hranjiva -gnojenja loze, moramo kao i za ostale biljne kulture odrediti koliËinu hranjiva kojeg trebamo, a za to je neophodno napraviti analizu zemlje.Bez obzira na veliËinu proizvodnje, svakako je potrebna analiza tla kao osnova i kljuË gnojenja.U laboratoriju Êe izmjeriti sve osnovne parametre tla, pH (reakciju tla -je li vaše tlo kiselo, neutralno ili lužnato), sadržaj fosfora, kalija i dodatno, po vašoj želji, a što se preporuËa, sadržaj organske tvari (humusa), sadržaj magnezija i bora.Za veÊe proizvoaËe bi svakako trebalo izmjeriti više parametara (sadržaj kalcija, sumpora, bakra, željeza, mangana, cinka, molibdena, taksturu tla, kationski izmjenjivaËki kapacitet).Takoer se preporuËa tijekom ljeta napraviti analizu lišÊa.Uzorke uzimamo u trenutku pojave vidljivih grozdiÊa -cvjetova, i na kraju srpnja tj.poËetkom kolovoza (tablica 1).Svi ti podaci nam koriste da vinovu lozu pravilno ishranimo i na taj naËin postignemo željenu kvalitetu i zadovoljavajuÊe prinose.Liebigov zakon minimuma kaže da je koliËina i kvaliteta prinosa ovisna o elementu koji je u minimumu.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.133 | 0.068 |
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".